System and process for multivariate adaptive regression splines classification for insurance underwriting suitable for use by an automated system

ABSTRACT

A method and system for automating the decision-making process used in underwriting of insurance applications is described. While this approach is demonstrated for insurance underwriting, it is broadly applicable to diverse decision-making applications in business, commercial, and manufacturing processes. A structured methodology is used based on a multi-model parallel network of multivariate adaptive regression splines (“MARS”) models to identify the relevant set of variables and their parameters, and build a framework capable of providing automated decisions. The parameters of the MARS-based decision system are estimated from a database consisting of a set of applications with reference decisions against each. Cross-validation and development/hold-out combined with re-sampling techniques are used to build a robust set of models that minimize the error between the automated system&#39;s decision and the expert human underwriter. Furthermore, this model building methodology can be used periodically to update and maintain the family of models if required to assure currency.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a system and process for underwritinginsurance applications, and more particularly to a system and processfor underwriting insurance applications based on multivariate adaptiveregression splines.

2. Description of Related Art

Classification is the process of assigning an input pattern to one of apredefined set of classes. Classification problems exist in manyreal-world applications, such as medical diagnosis, machine faultdiagnosis, handwriting character recognition, fingerprint recognition,and credit scoring, to name a few. Broadly speaking, classificationproblems can be categorized into two types: dichotomous classification,and polychotomous classification. Dichotomous classification deals withtwo-class classification problems, while polychotomous classificationdeals with classification problems that have more than two classes.

Classification consists of developing a functional relationship betweenthe input features and the target classes. Accurately estimating such arelationship is key to the success of a classifier. Insuranceunderwriting is one of these classification problems. The underwritingprocess consists of assigning a given insurance application, describedby its medical and demographic records, to one of the risk categories(also referred to as rate classes). A trained individual or individualstraditionally perform insurance underwriting. A given application forinsurance (also referred to as an “insurance application”) may becompared against a plurality of underwriting standards set by aninsurance company. The insurance application may be classified into oneof a plurality of risk categories available for a type of insurancecoverage requested by an applicant. The risk categories then affect thepremium paid by the applicant, e.g., the higher the risk category,higher the premium. A decision to accept or reject the application forinsurance may also be part of this risk classification, as risks above acertain tolerance level set by the insurance company may simply berejected.

Insurance underwriting often involves the use of a large number offeatures in the decision-making process. The features typically includethe physical conditions, medical information, and family history of theapplicant. Further, insurance underwriting frequently has large numberof risk categories (rate classes). The risk category of an insuranceapplication is traditionally determined by using a number ofrules/standards, which have the form of, for example, “if the value offeature x exceeds a, then the application can't be rate class C, i.e.,the application has to be lower than C”. Such manual underwriting,however, is not only time-consuming, but also often inadequate inconsistency and reliability. The inadequacy becomes more apparent as thecomplexity of insurance applications increases.

There can be a large amount of variability in the insurance underwritingprocess when individual underwriters perform it. Typically, underwritingstandards cannot cover all possible cases and variations of anapplication for insurance. The underwriting standards may even beself-contradictory or ambiguous, leading to an uncertain application ofthe standards. The subjective judgment of the underwriter will almostalways play a role in the process. Variation in factors such asunderwriter training and experience, and a multitude of other effectscan cause different underwriters to issue different, inconsistentdecisions. Sometimes these decisions can be in disagreement with theestablished underwriting standards of the insurance company, whilesometimes they can fall into a “gray area” not explicitly covered by theunderwriting standards.

Further, there may be an occasion in which an underwriter's decisioncould still be considered correct, even if it disagrees with the writtenunderwriting standards. This situation can be caused when theunderwriter uses his/her own experience to determine whether theunderwriting standards should be adjusted. Different underwriters maymake different determinations about when these adjustments are allowed,as they might apply stricter or more liberal interpretations of theunderwriting standards. Thus, the judgment of experienced underwritersmay be in conflict with the desire to consistently apply theunderwriting standards.

Other drawbacks may also exist.

SUMMARY OF THE INVENTION

According to an exemplary embodiment of the invention, a process forunderwriting an insurance application based on a plurality of previousinsurance applications and their associated underwriting decisionscomprises creating a plurality of binary classifiers based on astructured methodology of multivariate adaptive regression splines(“MARS”), where the plurality of binary classifiers are arranged in aparallel network, identifying a relevant set of MARS variables andparameters based on the plurality of previous insurance applications andtheir associated underwriting decisions, performing at least onecross-validation technique and at least one re-sampling technique on theplurality of previous insurance applications and their associatedunderwriting decisions, modifying the plurality of binary classifiersbased on the performance of the at least one cross-validation techniqueand the at least one re-sampling technique and utilizing the validatedparallel network for outputting a classification assignment for the atleast one new insurance application.

By way of a further exemplary embodiment, a process for underwriting aninsurance application based on a plurality of previous insuranceapplications and their associated underwriting decisions comprisesdigitizing the insurance application and the plurality of previousinsurance application underwriting decisions, generating a casebase ofthe plurality of previous insurance application underwriting decisions,creating a plurality of binary classifiers based on a structuredmethodology of multivariate adaptive regression splines (“MARS”), wherethe plurality of binary classifiers are arranged in a parallel network,identifying a relevant set of MARS variables and parameters based on theplurality of previous insurance applications and their associatedunderwriting decisions, performing at least one cross-validationtechnique and at least one re-sampling technique on the plurality ofprevious insurance applications and their associated underwritingdecisions, where the at least one re-sampling technique furthercomprises partitioning data from the previous insurance applications andtheir associated underwriting decisions into five groups of equal size,removing one of the five groups and combining the remaining four groupsin a development sample, modifying the plurality of binary classifiersbased on the performance of the at least one cross-validation techniqueand the at least one re-sampling technique, utilizing the validatedparallel network for outputting a classification assignment for the atleast one new insurance application and fusing the classificationassignment for the at least one insurance application with at least oneother classification assignment for the insurance application, where theat least one other classifier is generated by at least one otherclassifier.

According to an additional embodiment of the invention, a computerreadable medium having code for causing a processor to underwrite aninsurance application based on a plurality of previous insuranceapplication underwriting decisions is disclosed, where the mediumcomprises code for creating a plurality of binary classifiers based on astructured methodology of multivariate adaptive regression splines(“MARS”), where the plurality of binary classifiers are arranged in aparallel network, code for identifying a relevant set of MARS variablesand parameters based on the plurality of previous insurance applicationsand their associated underwriting decisions, code for performing atleast one cross-validation technique and at least one re-samplingtechnique on the plurality of previous insurance applications and theirassociated underwriting decisions, code for modifying the plurality ofbinary classifiers based on the performance of the at least onecross-validation technique and the at least one re-sampling techniqueand code for utilizing the validated parallel network for outputting aclassification assignment for the at least one new insuranceapplication.

An additional exemplary embodiment of the present invention involves acomputer readable medium having code for causing a processor tounderwrite an insurance application based on a plurality of previousinsurance application underwriting decisions, where the medium comprisescode for digitizing the insurance application and the plurality ofprevious insurance application underwriting decisions, code forgenerating a casebase of the plurality of previous insurance applicationunderwriting decisions, code for creating a plurality of binaryclassifiers based on a structured methodology of multivariate adaptiveregression splines (“MARS”), where the plurality of binary classifiersare arranged in a parallel network, code for identifying a relevant setof MARS variables and parameters based on the plurality of previousinsurance applications and their associated underwriting decisions, codefor performing at least one cross-validation technique and at least onere-sampling technique on the plurality of previous insuranceapplications and their associated underwriting decisions, where the atleast one re-sampling technique further comprises partitioning data fromthe previous insurance applications and their associated underwritingdecisions into five groups of equal size, removing one of the fivegroups and combining the remaining four groups in a development sample,code for modifying the plurality of binary classifiers based on theperformance of the at least one cross-validation technique and at leastone re-sampling technique, code for utilizing the validated parallelnetwork for outputting a classification assignment for the at least onenew insurance application, and code for fusing the classificationassignment for the at least one insurance application with at least oneother classification assignment for the insurance application, where theat least one other classifier is generated by at least one otherclassifier.

According to a further exemplary embodiment of the invention, a systemto underwrite an insurance application based on a plurality of previousinsurance application underwriting decisions comprises means forcreating a plurality of binary classifiers based on a structuredmethodology of multivariate adaptive regression splines (“MARS”), wherethe plurality of binary classifiers are arranged in a parallel network,means for identifying a relevant set of MARS variables and parametersbased on the plurality of previous insurance applications and theirassociated underwriting decisions, means for performing at least onecross-validation technique and at least one re-sampling technique on theplurality of previous insurance applications and their associatedunderwriting decisions, means for modifying the plurality of binaryclassifiers based on the performance of the at least onecross-validation technique and the at least one re-sampling techniqueand means for utilizing the validated parallel network for outputting aclassification assignment for the at least one new insuranceapplication.

According to an additional embodiment of the invention, a system tounderwrite an insurance application based on a plurality of previousinsurance application underwriting decisions comprises means fordigitizing the insurance application and the plurality of previousinsurance application underwriting decisions, means for generating acasebase of the plurality of previous insurance application underwritingdecisions, means for creating a plurality of binary classifiers based ona structured methodology of multivariate adaptive regression splines(“MARS”), where the plurality of binary classifiers are arranged in aparallel network, means for identifying a relevant set of MARS variablesand parameters based on the plurality of previous insurance applicationsand their associated underwriting decisions, means for performing atleast one cross-validation technique and at least one re-samplingtechnique on the plurality of previous insurance applications and theirassociated underwriting decisions, where the at least one re-samplingtechnique further comprises partitioning data from the previousinsurance applications and their associated underwriting decisions intofive groups of equal size, removing one of the five groups and combiningthe remaining four groups in a development sample, means for modifyingthe plurality of binary classifiers based on the performance of the atleast one cross-validation technique and the at least one re-samplingtechnique, means for utilizing the validated parallel network foroutputting a classification assignment for the at least one newinsurance application and means for fusing the classification assignmentfor the at least one insurance application with at least one otherclassification assignment for the insurance application, where the atleast one other classifier is generated by at least one otherclassifier.

By way of a further example, a system to underwrite an insuranceapplication based on a plurality of previous insurance applicationunderwriting decisions comprises a classifier module for creating aplurality of binary classifiers based on a structured methodology ofmultivariate adaptive regression splines (“MARS”), where the pluralityof binary classifiers are arranged in a parallel network, anidentification module for identifying a relevant set of MARS variablesand parameters based on the plurality of previous insurance applicationsand their associated underwriting decisions, a processor for performingat least one cross-validation technique and at least one re-samplingtechnique on the plurality of previous insurance applications and theirassociated underwriting decisions, and where the classifier modulemodifies the plurality of binary classifiers based on the performance ofthe at least one cross-validation technique and at least one re-samplingtechnique to build a robust set of binary classifiers, and an outputmodule utilizing the validated parallel network for outputting aclassification assignment for the at least one new insuranceapplication.

According to another exemplary embodiment of the present invention, asystem to underwrite an insurance application based on a plurality ofprevious insurance application underwriting decisions comprises adigitizer for digitizing the insurance application and the plurality ofprevious insurance application underwriting decisions, a storage modulefor creating a casebase of the plurality of previous insuranceapplication underwriting decisions, where the processing occurs based atleast in part on the contents of the casebase, a classifier module forcreating a plurality of binary classifiers based on a structuredmethodology of multivariate adaptive regression splines (“MARS”), wherethe plurality of binary classifiers are arranged in a parallel network,an identification module for identifying a relevant set of MARSvariables and parameters based on the plurality of previous insuranceapplications and their associated underwriting decisions, a processorfor performing at least one cross-validation technique and at least onere-sampling technique on the plurality of previous insuranceapplications and their associated underwriting decisions, where theclassifier module modifies the plurality of binary classifiers based onthe performance of the at least one cross-validation technique and atleast one re-sampling technique to build a robust set of binaryclassifiers and where the at least one re-sampling technique furthercomprises partitioning data from the previous insurance applications andtheir associated underwriting decisions into five groups of equal size,removing one of the five groups and combining the remaining four groupsin a development sample, an output module utilizing the validatedparallel network for outputting a classification assignment for the atleast one new insurance application, and a fuser for fusing theclassification assignment for the at least one insurance applicationwith at least one other classification assignment for the insuranceapplication, where the at least one other classifier is generated by atleast one other classifier.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates the architecture of a quality assurance system basedon the fusion of multiple classifiers according to an embodiment of theinvention.

FIG. 2 illustrates a table of an outer product using the function T(x,y)according to an embodiment of the invention.

FIG. 3 illustrates the disjointed rate classes within the universe ofrate classes according to an embodiment of the invention.

FIG. 4 illustrates the results of the intersections of the rate classesand the universe according to an embodiment of the invention.

FIGS. 5-9 illustrate the results of T-norm operators according to anembodiment of the invention.

FIGS. 10-14 illustrate the normalized results of T-norm operatorsaccording to an embodiment of the invention.

FIG. 15 illustrates a summary of the fusion of two classifiers accordingto an embodiment of the invention.

FIG. 16 illustrates a penalty matrix for a fusion module according to anembodiment of the invention.

FIG. 17 illustrates a summary of the fusion of two classifiers withdisagreement according to an embodiment of the invention.

FIG. 18 illustrates a summary of the fusion of two classifiers withagreement and discounting according to an embodiment of the invention.

FIGS. 19-23 illustrate the results of T-norm operators according to anembodiment of the invention.

FIGS. 24-28 illustrate the normalized results of T-norm operatorsaccording to an embodiment of the invention.

FIG. 29 illustrates a Dempster-Schaefer penalty matrix according to anembodiment of the invention.

FIG. 30 illustrates a comparison matrix according to an embodiment ofthe invention.

FIG. 31 illustrates fusion as a function of a confidence threshold fornon-nicotine cases according to an embodiment of the invention.

FIG. 32 illustrates fusion as a function of a confidence threshold fornicotine cases according to an embodiment of the invention.

FIG. 33 illustrates a Venn diagram for fusion for non-nicotine casesaccording to an embodiment of the invention.

FIG. 34 illustrates a Venn diagram for fusion for nicotine casesaccording to an embodiment of the invention.

FIG. 35 is a flowchart that illustrates an outlier detector according toan embodiment of the invention.

FIG. 36 illustrates an outlier detector used in quality assuranceaccording to an embodiment of the invention.

FIG. 37 illustrates a plot of two features for insurance applicationsaccording to an embodiment of the invention.

FIG. 38 is a flowchart that illustrates a tuning process according to anembodiment of the invention.

FIG. 39 is a flowchart that illustrates a classification processaccording to an embodiment of the invention.

FIG. 40 illustrates a comparison matrix according to an embodiment ofthe invention.

FIG. 41 illustrates a comparison matrix for a modified process accordingto an embodiment of the invention.

FIG. 42 is a flowchart that illustrates a multi-variate adaptiveregression splines (“MARS”) process according to an embodiment of theinvention.

FIG. 43 is a histogram that illustrates decision boundaries according toan embodiment of the invention.

FIG. 44 illustrates a parallel network implementation according to anembodiment of the invention.

FIG. 45 illustrates a comparison matrix according to an embodiment ofthe invention.

FIG. 46 illustrates an annotated comparison matrix according to anembodiment of the invention.

FIG. 47 illustrates a performance of MARS models using five partitionsaccording to an embodiment of the invention.

FIG. 48 illustrates minimum, maximum, and average performances of anetwork of MARS models according to an embodiment of the invention.

FIG. 49 illustrates a piecewise-continuous classification boundary in afeature space according to an embodiment of the invention.

FIG. 50 illustrates a multi-class neural network decomposed intomultiple binary classifiers according to an embodiment of the invention.

FIG. 51 illustrates an architecture for a neural network classifieraccording to an embodiment of the invention.

FIG. 52 illustrates a confusion matrix before post-processing accordingto an embodiment of the invention.

FIG. 53 illustrates a confusion matrix after post-processing accordingto an embodiment of the invention.

FIG. 54 illustrates performance before post-processing according to anembodiment of the invention.

FIG. 55 illustrates performance after post-processing according to anembodiment of the invention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

A system and process for underwriting of insurance applications that issuitable for use by a computer rather than by human intervention isdescribed. The system and process make use of existing risk assignmentsmade by human underwriters to categorize new applications in terms ofthe risk involved. One technical effect of the invention is to providean automated process for consistent and accurate underwriting decisionsfor insurance applications. Various aspects and components of thissystem and process are described below.

It will be recognized, however, that the principles disclosed herein mayextend beyond the realm of insurance underwriting and that it may beapplied to any risk classification process, of which the determinationof the proper premium to cover a given risk (i.e. insuranceunderwriting) is just an example. Therefore the ultimate domain of thisinvention may be considered risk classification.

1. Fusion Module

An aspect of the invention provides a system and process for fusing acollection of classifiers used for an automated insurance underwritingsystem and/or its quality assurance. While the design method isdemonstrated for quality assurance of automated insurance underwriting,it is broadly applicable to diverse decision-making applications inbusiness, commercial, and manufacturing processes. A process of fusingthe outputs of a collection of classifiers is provided. The fusion cancompensate for the potential correlation among the classifiers. Thereliability of each classifier can be represented by a static or dynamicdiscounting factor, which will reflect the expected accuracy of theclassifier. A static discounting factor represents a prior expectationabout the classifier's reliability, e.g., it might be based on theaverage past accuracy of the model. A dynamic discounting represents aconditional assessment of the classifier's reliability, e.g., whenever aclassifier bases its output on an insufficient number of points, theresult is not reliable. Hence, this factor could be determined from thepost-processing stage in each model. The fusion of the data willtypically result in some amount of consensus and some amount of conflictamong the classifiers. The consensus will be measured and used toestimate a degree of confidence in the fused decisions.

According to an embodiment of the invention, a fusion module (alsoreferred to as a fusion engine) combines the outputs of several decisionengines (also referred to as classifiers or components of the fusionmodule) to determine the correct rate class for an insuranceapplication. Using a fusion module with several decision engines mayenable a classification to be assigned with a higher degree ofconfidence than is possible using any single model. According to anembodiment of the invention, a fusion module function may be part of aquality assurance (“QA”) process to test and monitor a productiondecision engine (“PDE”) that makes the rate class assignment inreal-time. At periodic intervals, e.g., every week, the fusion moduleand its components may review the decisions made by the PDE during theprevious week. The output of this review will be an assessment of thePDE performance over that week, as well as the identification of caseswith different level of decision quality.

The fusion module may permit the identification of the best cases ofapplication classification, e.g., those with high-confidence,high-consensus decisions. These best cases in turn may be likelycandidates to be added to the set of test cases used to tune the PDE.Further, the fusion module may permit the identification of the worstcases of application classification, e.g., those with low-confidence,low-consensus decisions. These worst cases may be likely candidates tobe selected for a review by an auditing staff and/or by seniorunderwriters.

A fusion module may also permit the identification of unusual cases ofapplication classification, e.g., those with unknown confidence in theirdecisions, for which the models in the fusion module could not make anystrong commitment or avoided the decision by routing the insuranceapplication to a human underwriter. These cases may be candidates for ablind review by senior underwriters. In addition, a fusion module mayalso permit an assessment of the performance of the PDE, by monitoringthe PDE accuracy and variability over time, such as monitoring thestatistics of low, borderline and high quality cases as well as theoccurrence of unusual cases. These statistics can be used as indicatorsfor risk management.

According to an embodiment of the invention, a fusion module mayleverage the fact that except for the unusual situation where all thecomponents (e.g., models) contain the same information (e.g., an extremecase of positive correlation), each component should provide additionalinformation. This information may either corroborate or refute theoutput of the other modules, thereby supporting either a measure ofconsensus, or a measure of conflict. These measures may define aconfidence in the result of the fusion. In general, the fusion of thecomponents' decisions may provide a more accurate assessment than thedecision of each individual component.

The fusion module is described in relation to various types of decisionengines, including a case-based decision engine, a dominance-baseddecision engine, a multi-variate adaptive regression splines engine, anda neural network decision engine respectively. However, the fusionmodule may use any type of decision engine. According to an embodimentof the invention, the fusion module will support a quality assuranceprocess for a production decision engine. However, it is understood thatthe fusion module could be used for a quality assurance process for anyother decision making process, including a human underwriter.

According to an embodiment of the invention, a general method for thefusion process, which can be used with classifiers that may exhibit anykind of (positive, neutral, or negative) correlation with each other,may be based on the concept of triangular norms (“T-norm”), amulti-valued logic generalization of the Boolean intersection operator.The fusion of multiple decisions, produced by multiple sources,regarding objects (e.g., classes) defined in a common framework (e.g.,the universe of discourse) consists of determining the underlying ofdegree of consensus for each object (e.g., class) under consideration,i.e., the intersections of their decisions. With the intersections ofmultiple decisions, possible correlation among the sources needs to betaken into account to avoid under-estimates or over-estimates. This isdone by the proper selection of a T-norm operator.

According to an embodiment of the invention, each model is assumed to besolving the same classification problem. Therefore, the output of eachclassifier is a weight assignment that represents the degree to which agiven class is selected. The set of all possible classes, referred to asU, represents the common universe of all answers that can be consideredby the classifiers. The assignment of weights to this universerepresents the classifier's ignorance (i.e., lack of commitment to aspecific decision). This is a discounting mechanism that can be used torepresent the classifier's reliability.

According to an embodiment of the invention, the outputs of theclassifiers may be combined by selecting the generalized intersectionoperator (e.g., the T-norm) that better represents the possiblecorrelation between the classifiers. With this operator, the assignmentsof the classifiers are intersected and a derived measure of consensus iscomputed. This fusion may be performed in an associative manner, e.g.,the output of the fusion of the first two classifiers is combined withthe output of the third classifier, and so on, until all availableclassifiers have been considered. At this stage, the final output may benormalized (e.g., showing the degree of selection as a percentage).Further, the strongest selection of the fusion may be identified andqualified with its associated degree of confidence.

Thus, according to an embodiment of the invention, a fusion module onlyconsiders weight assignments made either to disjoint subsets thatcontain a singleton (e.g., a rate class) or to the entire universe ofclasses U (e.g., the entire set of rate classes), as will be describedin greater detail below. Once compensation has been made for correlationand fusion has been performed, the degree of confidence C is computedamong the classifiers and used to qualify the decision obtained from thefusion. Further, the confidence measure and the agreement ordisagreement of the fusion module's decision is used with the productionengine's decision to assess the quality of the production engine. As aby-product, the application cases may be labeled in terms of thedecision confidence. Thus, cases with low, high, or unknown confidencemay be used in different ways to maintain and update the productionengine.

Other types of aggregation could be used, but would need to beassociative, compensate for correlation, accommodate the discounting ofclassifiers, and generate a confidence measure of the combined decision,properties that are not directly satisfied. A particular case may be aDempster-Shafer (“DS”) fusion rule. The DS fusion rule requires theclassifiers to be evidentially independent, i.e., the errors of oneclassifier must be uncorrelated with those of another one. Furthermore,the DS paradigm does not allow us to represent the ordering among theclasses, typical of the insurance underwriting process. This orderingimplies that there could be minor differences (such as the selection oftwo adjacent classes) and major differences (such as the selection ofdifferent classes at the extreme of their range). Therefore, theconflict between two sources is a gradual one, rather than a binary one(hit/miss). Finally, in DS theory, the classifiers' outputs areconsidered probability assignments.

Triangular norms (T-norms) and Triangular conorms (T-conorms) are themost general families of binary functions that satisfy the requirementsof the conjunction and disjunction operators, respectively. T-normsT(x,y) and T-conorms S(x,y) are two-place functions that map the unitsquare into the unit interval, i.e., T(x,y): [0,1]×[0,1]→[0,1] andS(x,y): [0,1]×[0,1]→[0,1]. They are monotonic, commutative andassociative functions. Their corresponding boundary conditions, i.e.,the evaluation of the T-norms and T-conorms at the extremes of the [0,1]interval, satisfy the truth tables of the logical AND and OR operators.They are related by the DeMorgan duality, which states that if N(x) is anegation operator, then the T-conorm S(x,y,) can be defined asS(x,y)=N(T(N(x), N(y))).

As described in Bonissone and Decker (1986) the contents of which areincorporated by reference in their entirety, six parameterized familiesof T-norms and their dual T-conorms may be used. Of the sixparameterized families, one family was selected due to its completecoverage of the T-norm space and its numerical stability. This familyhas a parameter p. By selecting different values of p, T-norms withdifferent properties can be instantiated, and thus may be used in thefusion of possibly correlated classifiers.

Various articles discuss the fusion and the different featuresassociated therewith, include proofs as to the development of algorithmsassociated with the present invention. Chibelushi et al. (Chibelushi, C.C., Deravi, F., and Mason, J. S. D., “Adaptive Classifier Integrationfor Robust Pattern Recognition,” IEEE Transactions on Systems, Man, andCybernetics, vol. 29, no. 6, 1999, the contents of which areincorporated herein by reference) describe a linear combination methodfor combining the outputs of multiple classifiers used in speakeridentification applications.

Fairhurst and Rahman (Fairhurst, M. C., and Rahman, A. F. R., “Enhancingconsensus in multi expert decision fusion,” IEE Proc.-Vis. Image SignalProcess, vol. 147, no. 1, 2000, the contents of which are incorporatedherein by reference) describe ENCORE, a multi-classifier fusion systemfor enhancing the performance of individual classifiers for patternrecognition tasks, specifically, the task of hand written digitrecognition. Kuncheva and Jain (Kuncheva, L. I., and Jain, L. C.,“Designing Classifier Fusion Systems by Genetic Algorithms,” IEEETransactions on Evolutionary Computation, vol. 4, no. 4, 2000, thecontents of which are incorporated herein by reference) describe agenetic algorithm approach to the design of fusion of multipleclassifiers.

Xu et al. (Xu, L., Krzyzak, A., and Suen, C. Y., “Methods of CombiningMultiple Classifiers and Their Applications to Handwriting Recognition,”IEEE Transactions on Systems, Man, and Cybernetics, vol. 22, no. 3,1992, the contents of which are incorporated herein by reference)describe several standard approaches for classifier decision fusion,including the Dempster-Shafer approach, and demonstrate fusion forhandwritten character recognition.

Arthur Dempster (A. P. Dempster, “Upper and lower probabilities inducedby a multivalued mapping,” Annals of Mathematical Statistics,38:325-339, 1967, the contents of which are incorporated herein byreference) describes a calculus based on lower and upper probabilitybounds. Dempster's rule of combination describes the pooling of sourcesunder the assumption of evidential independence. Glenn Shafer (G.Shafer, “A Mathematical Theory of Evidence”, Princeton University Press,Princeton, N.J., 1976, the contents of which are incorporated herein byreference) describes the same calculus discovered by Dempster, butstarting from a set of super-additive belief functions that areessentially lower bounds. Shafer derives the same rule of combination asDempster. Enrique Ruspini (E. Ruspini, “Epistemic logic, probability,and the calculus of evidence. Proc. Tenth Intern. Joint Conf. onArtificial Intelligence, Milan, Italy, 1987, the contents of which areincorporated herein by reference) goes on to describe a possible-worldsemantics for Dempster-Shafer theory.

B. Schweizer and A. Sklar (B. Schweizer and A. Sklar, “AssociativeFunctions and Abstract Semi-Groups”, Publicationes MathematicaeDebrecen, 10:69-81, 1963, the contents of which are incorporated hereinby reference) describe a parametric family of triangular T-normfunctions that generalize the concept of intersection in multiple-valuedlogics. Piero Bonissone and Keith. Decker (P. P. Bonissone and K.Decker, “Selecting Uncertainty Calculi and Granularity: An Experiment inTrading-off Precision and Complexity” in Kanal and Lemmer (editors)Uncertainty in Artificial Intelligence, pages 217-247, North-Holland,1986, the contents of which are incorporated herein by reference)describe an experiment based on Schweizer and Sklar's parameterizedT-norms. They show how five triangular norms can be used to represent aninfinite number of t-norm for some practical values of informationgranularity. Piero Bonissone (P. P. Bonissone, “Summarizing andPropagating Uncertain Information with Triangular Norms”, InternationalJournal of Approximate Reasoning, 1(1):71-101, January 1987, thecontents of which are incorporated herein by reference) also describesthe use of Triangular norms in dealing with uncertainty in expertsystem, Specifically he shows the use Triangular norms to aggregate theuncertainty in the left-hand side of production rules and to propagateit through the firing and chaining of production rules.

FIG. 1 illustrates the architecture of a quality assurance system basedon the fusion of multiple classifiers according to an embodiment of theinvention. These classifiers may include case-based reasoning model(described in U.S. patent application Ser. Nos. 10/170,471 and10/171,190, the contents of which are incorporated herein by reference),a multivariate adaptive regression splines model (hereinafter alsoreferred to as “MARS”), a neural network model and a dominance-basedmodel. The MARS, neural networks, and dominance-based models are alldescribed in greater detail below.

System 100, as illustrated in FIG. 1, includes a number of qualityassurance decision engines 110. In the embodiment illustrated in FIG. 1,the quality assurance decision engines 110 comprise a case-basedreasoning decision engine 112, a MARS decision engine 114, a neuralnetwork decision engine 116, and a dominance-based decision engine 118.It is understood, however, that other types of quality assurancedecision engines 110 could be used in addition to and/or as substitutesfor those listed in the embodiment of the invention illustrated in FIG.1.

Post processing modules 122, 124, 126, and 128 receive the outputs fromthe various quality assurance decision engines 120 and performprocessing on the outputs. The results of the post-processing are inputinto a multi-classifier fusion module 130. The multi-classifier fusionmodule 130 then outputs a fusion rate class decision 135 and a fusionconfidence measure 140, which are input into comparison module 150.

A fuzzy logic rule-based production engine 145 outputs a production rateclass decision 147 and a production confidence measure 149, which arethen input into comparison module 150. After a comparison has been madebetween the production rate class decision 147 and the fusion rate classdecision 135, and the production confidence measure 149 and the fusionconfidence measure 140, a compared rate class decision 151 and acompared confidence measure 153 are output by comparison module 150. Anevaluation module 155 evaluates the case confidence and consensusregarding the compared rate class 151 and the compared confidencemeasure 153. Those cases evaluated as “worst cases” are stored in casedatabase 160, and may be candidates for auditing. Those cases evaluatedas “unusual cases” are stored in case database 165, and may becandidates for standard underwriting. Those cases evaluated as “bestcases” are stored in case database 170, and may be candidates for usingwith the test sets. The outlier detector and filter 180 may ensure thatany new addition to the best-case database 170 will be consistent (inthe dominance sense described below) with the existing cases, preventinglogical outliers from being used. System 100 of FIG. 1 will now bedescribed in greater detail below.

According to an embodiment of the invention, the fusion process asdisclosed in FIG. 1 includes four general steps. These steps are: (1)collection, discounting and post-processing of modules' outputs; (2)determination of a combined decision via the associative fusion of themodules' outputs; (3) determination of degree of confidence; and (4)identification of cases that are candidates for test set, auditing, orstandard reference decision process, via the comparison module 150.These steps will now be described in greater detail below.

Each quality assurance decision module 110 generates an output vectorI=[I(1), I₍₂₎, . . . I_((N+1))] where I_((i))∈[0,M], where M is a largereal value and N is the number of rate classes. In the embodiment of theinvention illustrated in FIG. 1, each vector I is identified by asuperscript associated with the quality assurance decision module 120that generates the vector. Therefore, I^(C) is generated by case-basedreasoning decision engine 112, I^(M) is generated by MARS decisionengine 114, I^(N) neural network decision engine 116, and I^(D) isgenerated by dominance-based decision engine 118. Further, each entryI_((i)), for i=1, . . . , N, can be considered as the (un-normalized)degree to which the case could be classified in rate class i. The lastelement, I_((N+1)) indicates the degree to which the case cannot bedecided and the entire universe of rate classes is selected.

For illustration purpose, assume that five rate classes are used, i.e.,N=5, namely:

-   -   Rate Class={Preferred Best, Preferred, Select, Standardplus,        Standard, No Decision (Send to UW)}

By way of this example, assume that the output of the first classifier(CBE) is: I^(C)=[0.3, 5.4, 0.3, 0, 0, 0]. This indicates that the secondrate class (e.g., Preferred) is strongly supported by the classifier.Normalizing I^(C) to see the support as a percentage of the overallweights, Î^(C)=[0.05,0.9,0.05,0,0,0], shows that 90% of the weights isassigned to the second rate class.

Further, to represent partial ignorance, i.e., cases in which theclassifier does not have enough information to make a more specific rateclassification, discounting may be used. According to an embodiment ofthe invention, discounting may involve the assignment of some weight tothe last element, corresponding to the universe U=(No Decision: Send toUW). For example, the previous assignment of I^(C) could be changed suchthat I^(C)=[0.3, 1.4, 0.3, 0, 0, 4], and its normalized assignment wouldbe Î^(C)=[0.05,0.23,0.05,0,0,0.67]. This example shows how 67% of theweights have now been assigned to the universe of discourse U (theentire set of rate classes). This feature allows a representation of thelack of commitment by individual modules. According to an embodiment ofthe invention, if it is necessary to discount a source because it is notbelieved to be credible, competent, or reliable enough in generating thecorrect decision, a portion of the weight is transferred to the universeof discourse (e.g., “any of the above categories”). The determination ofthe discount may be derived from meta-knowledge, as opposed toobject-knowledge. Object knowledge is the level at which each classifieris functioning, e.g., mapping input vectors into decision bins.Meta-knowledge is reasoning about the classifiers' performance overtime. Discounting could be static or dynamic. Static discounting may beused a priori to reflect historical (accuracy) performance of eachclassifier. Dynamic discounting may be determined by evaluating a set ofrules, whose Left Hand Side (“LHS”) defines a situation, characterizedby a conjunct of conditions, and whose Right Hand Side (“RHS”) definesthe amount by which to discount whichever output is generated by theclassifier. According to an embodiment of the invention, postprocessingmay be used to detect lack of confidence in a source. When this happens,all the weights may be allocated to the universe of discourse, i.e.,refrain from making any decision.

According to an embodiment of the invention, each decision engine modelwill independently perform a post-processing step. For purposes ofillustration, the post processing used for the neural network model willbe described. According to an embodiment of the invention, to furtherimprove the classification performance of a neural network module, somepost-processing techniques may be applied to the outputs of theindividual networks, prior to the fusion process. For example, if thedistribution of the outputs did not meet certain pre-defined criteria,no decision needs to be made by the classifier. Rather, the case will becompletely discounted by allocating all of the weights to the entireuniverse of discourse U. The rationale for this particular example isthat if a correct decision cannot be made, it would be better not tomake any decision rather than making a wrong decision. Considering theoutputs as discrete membership grades for all rate classes, the fourfeatures that characterize the membership grades may be defined asfollows, where N is the number of rate classes and I the membershipfunction, i.e., the output of the classifier.

-   -   1. Cardinality

$C = {\sum\limits_{1}^{N}{I(i)}}$

-   -   2. Entropy

${E = {\frac{1}{E_{\max}}{\sum\limits_{1}^{N}{{I(i)} \times \log\;\left( {I(i)} \right)}}}},{{{where}\mspace{14mu} E_{\max}} = {{- \log}\;\left( \frac{1}{N} \right)}}$

-   -   3. Difference between the highest and the second highest values        of outputs.

D = I_(max1) − I_(max2)

-   -   4. Separation between the rank orders of the highest and the        second highest values of outputs

S = RankOrder(I_(max1)) − RankOrder(I_(max2))

With the features defined for characterizing the network outputs, thefollowing two-step criteria may be used to identify the cases with weakdecisions:Step 1: C<τ₁ OR C>τ₂ OR E>τ₃Step 2: D<τ₄ AND S≦1

where τ₁, τ₂, τ₃, and τ₄ are the thresholds. The value of the thresholdsis typically dataset dependent. However, in some embodiments, the valueof the thresholds may be independent of the dataset. In the presentexample related to a neural network classifier module (which in turn isdescribed in greater detail below), the value of the thresholds may befirst empirically estimated and then fine-tuned by a global optimizer,such as an evolutionary algorithm. As part of this example, the finalnumbers are shown below in Table 1. Other optimization methods may alsobe used to obtain the thresholds.

TABLE 1 Non- nicotine Nicotine Thresholds Users Users τ₁ 0.50 0.30 τ₂2.00 1.75 τ₃ 0.92 0.84 τ₄ 0.10 0.21

Thus, post-processing may be used to identify those cases for which themodule's output is likely to be unreliable. According to an embodimentof the invention, rather than rejecting such cases, the model assignmentof normalized weights to rate classes may be discounted by assigningsome or all of those weights to the universe of discourse U.

As described previously, the fusion module 150 may perform the step ofdetermining a combined decision via the associative fusion of thedecision engine models' outputs. According to an embodiment of theinvention, any general method that can be used to fuse the output ofseveral classifiers may be used. The fusion method may also beassociative, meaning that given three or more classifiers, any two ofthe classifiers may be fused, then fusing the results with the thirdclassifier, and so on, regardless of the order.

By way of example of determining a combined decision, define mclassifiers S₁, . . . S_(m), such that the output of classifier S_(j) isthe vector I^(j) showing the normalized decision of such classifier tothe N rate classes. Recall the last (N+1)^(th) element represents theclassifier's lack of commitment, i.e., I^(j)=[I^(j)(1), I^(j)(2), . . ., I^(j)(N+1)], where:

${{I^{j}(i)} \in {\left\lbrack {0,1} \right\rbrack\mspace{14mu}{and}\mspace{14mu}{\sum\limits_{i = 1}^{N + 1}{I^{j}(i)}}}} = 1$

The un-normalized fusion of the outputs of two classifiers S₁and S₂ isfurther defined as:F(I ¹ ,I ²)=Outerproduct(I ¹ ,I ² ,T)=A

where the outer-product is a well-defined mathematical operation, whichin this case takes as arguments the two N-dimensional vectors I¹ and I²and generates as output the NxN dimensional array A. Each element A(ij)is the result of applying the operator T to the corresponding vectorelements, namely I¹(i) and I²(j), e.g.,A(i,j)=T[I ¹(i)I ²(j)]

and as illustrated in FIG. 2. Matrix 200 illustrates classes 202 andvalues 204 for vector I¹ and classes 206 and values 208 for vector I².Intersection 210 illustrates one intersection between the vector I¹ andvector I². Other intersections and representations may also be used.

The operator T(x,y) may be referred to as a Triangular Norm. TriangularNorms (also referred to as “T-norms”) are general families of binaryfunctions that satisfy the requirements of the intersection operators.T-norms are functions that map the unit square into the unit interval,i.e., T: [0,1]×[0,1]→[0.2]. T-norms are monotonic, commutative andassociative. Their corresponding boundary conditions, i.e., theevaluation of the T-norms at the extremes of the [0,1] interval, satisfythe truth tables of the logical AND operator.

As there appear to be an infinite number of T-norms, the five mostrepresentative T-norms for some practical values of informationgranularity may be selected. According to an embodiment of theinvention, the five T-norms selected are:

T-Norm Correlation Type T₁(x, y) = max(0, x + y − 1) Extreme case ofnegative correlation T_(1.5)(x, y) = max(0, x^(0.5) + y^(0.5) − 1)²Partial case of negative correlation T₂(x, y) = x * y No correlationT_(2.5)(x, y) = (x⁻¹ + y⁻¹ − 1)⁻¹ Partial case of positive correlationT₃(x, y) = min(x, y) Extreme case of positive correlation

The selection of the best T-norm to be used as an intersection operationin the fusion of the classifiers may depend on the potential correlationamong the classifiers to be fused. For example, T3 (the minimumoperator) may be used when one classifier subsumes the other one (e.g.,extreme case of positive correlation). T2 may be selected when theclassifiers are uncorrelated (e.g., similar to the evidentialindependence in Dempster-Shafer). T1 may be used if the classifiers aremutually exclusive (e.g., extreme case of negative correlation). Theoperators T_(1.5) and T_(2.5) may be selected when the classifiers showintermediate stages of negative or positive correlation, respectively.Of course, it will be understood by one of ordinary skill in the artthat other T-norms may also be used. However, for the purposes of thepresent invention, these five T-norms provide a good representation ofthe infinite number of functions that satisfy the T-norm properties.

Because the T-norms are associative, so is the fusion operator, i.e.,F(I ¹ ,F(I ² ,I ³))=F(F(I ¹ , I ²),I ³)

Each element A(i,j) represents the fused assignment of the twoclassifiers to the intersection of rate classes r_(i) and r_(j). FIG. 3illustrates that each rate class is disjointed and that U 300, is theuniverse of all (rate) classes. In this example, rate classes r₁ 302, r₂304 to r_(n) 306 are shown. Given that the rate classes are disjoint,there are five possible situations:

(a) When i=j and i<(N+1) then r_(i)∩r_(j)=r_(j)∩r_(i)=r_(i)

(b) When i=j and i=(N+1) then r_(i)∩r_(j)=U (the universe of rateclasses)

(c) When i≠j and i<(N+1) and j<(N+1) then r_(i)∩r_(j)=φ(the empty set)

(d) When i≠j and i=(N+1) then U∩r_(j)=r_(j)

(e) When i≠k and j=(N+1) then r_(i)∩U=r_(i)

FIG. 4 depicts a chart 400 that illustrates the result of theintersections of the rate classes and the universe U, according to anembodiment of the invention. The chart demonstrates the intersectionaccording to those situations set forth above, such that when situation(a) occurs, the results are tabulated in the main diagonal identified as410 in FIG. 4. Further, when situation (b) occurs, the results aretabulated in the appropriate areas identified as 420 in FIG. 4. Whensituation (c) occurs, the results are tabulated in the appropriate areasidentified as 430, while when situations (d) or (e) occur, the resultsare tabulated in the appropriate areas identified as 440 in FIG. 4. Byway of example, when one application is rated r1 in the first instanceand r2 in the second instance, the intersection may be tabulated at 450,where the column for r1 and the row for r2 intersect. In this example,the intersection of r1 and r2 is the empty set φ. The decisions for eachrate class can be gathered by adding up all the weights assigned tothem. According to the four possible situations described above, weightsmay be assigned to a specific rate class only in situation a) and d), asillustrated in FIG. 4. Thus, there will be:Weight (r _(i))=A(ii)+A(i,N+1)+A(N+1,i)Weight (U)=A(N+1,N+1)

To illustrate the fusion operator based on T-norms, an example will nowbe described. Assume thatI ¹=[0.8, 0.15, 0.05, 0, 0, 0] and I ²=[0.9, 0.05, 0.05, 0, 0, 0]

This indicates that both classifiers are showing a strong preference forthe first rate class (e.g., “Preferred Best”) as they have assigned them0.8 and 0.9, respectively. Fusing these classifiers using each of thefive T-norm operators defined above will generate the correspondingmatrices A that are shown in the tables in FIGS. 5-9, such that FIG. 5illustrates an extreme positive correlation, FIG. 6 illustrates apartial positive correlation, FIG. 7 illustrates no correlation, FIG. 8illustrates a partial negative correlation and FIG. 9 illustrates anextreme negative correlation. If the results are normalized so that thesum of the entries is equal to one, the matrices Â are generated, asshown in the tables in FIGS. 10-14 in a manner corresponding to theun-normalized results. During the process, the un-normalized matrices A(FIGS. 5-9) may be used to preserve the associative property. At theend, the normalized matrices Â are used (FIGS. 10-14). Using theexpressions for weights of a rate class, the final weights for the Nrate classes and the universe U from FIGS. 10-14 can be computed. Anillustration of the computation of the final weights is illustrated inthe chart of FIG. 15. Chart 1500 illustrates the five classes 1510, thefive T-norms 1520, and the fused intersection results 1530.

According to an embodiment of the invention, the confidence in thefusion may be calculated by defining a measure of the scattering aroundthe main diagonal. The more the weights are assigned to elements outsidethe main diagonal, the less is the measure of the consensus among theclassifiers. This concept may be represented by defining a penaltymatrix P=[P(i,j)], of the form:

${P\left( {i,j} \right)} = \left\{ \begin{matrix}{\max\left( {0,\left( {1 - {W*{{i - j}}}} \right)} \right)}^{d} & {{{for}\mspace{14mu} 1} \leq i \leq {N\mspace{14mu}{and}\mspace{14mu} 1} \leq j \leq N} \\1 & {{{for}\mspace{14mu} i} = {{\left( {N + 1} \right)\mspace{14mu}{or}\mspace{14mu} j} = \left( {N + 1} \right)}}\end{matrix} \right.$

This function rewards the presence of weights on the main diagonal,indicating agreement between the two classifiers, and penalizes thepresence of elements off the main diagonal, indicating conflict. Theconflict increases in magnitude as the distance from the main diagonalincreases. For example, for W=0.2 and d=5 we have the penalty matrix setforth in FIG. 16. Matrix 1600 intersects the column classes 1610 withthe row classes 1620 to determine the appropriate penalty.

Other functions penalizing elements off the main diagonal, such as anysuitable non-linear function of the distance from the main diagonal,i.e., the absolute value |i-j|, could also be used. The penalty functionis used because the conflict may be gradual, as the (rate) classes havean ordering. Therefore, the penalty function captures the fact that thediscrepancy between rate classes r₁, and r₂ is smaller than then thediscrepancy between r₁ and r₃. The shape of the penalty matrix P in FIG.16 captures this concept, as P1600 shows that the confidence decreasesnon-linear with the distance from the main diagonal. A measure of thenormalized confidence Ĉ is the sum of element-wise products between Âand P 1600, e.g.:

$\hat{C} = {{{Normalized}\mspace{14mu}{Confidence}\;\left( {\hat{A},P} \right)} = {\sum\limits_{i = 1}^{N + 1}{\sum\limits_{j = 1}^{N + 1}{{\hat{A}\left( {i,j} \right)}*{P\left( {i,j} \right)}}}}}$

where Â is the normalized fusion matrix. The results of the fusion ofclassifiers S1 and S2, using each of the five T-norms with theassociated normalized confidence measure, are shown in FIG. 15.

In a situation in which there is a discrepancy between the twoclassifiers, this fact may be captured by the confidence measure. Forinstance, consider a situation different from the assignment illustratedin FIGS. 5-14, in which the classifiers agreed to select the first rateclass. Now e.g., assume that the two classifiers are showing strongpreferences for different rate classes, the first classifier isselecting the second rate class, while the second classifier is favoringthe first class:I ¹=[0.15, 0.85, 0.05, 0, 0, 0] and I ²=[0.9, 0.05, 0.05, 0, 0, 0]

The results of their fusion are summarized in the table of FIG. 17,where the chart 1700 illustrates the rate classes 1710, the T-norms 1720and the fused intersection results 1730. None of the rate classes have ahigh weight and the normalized confidence has dropped.

According to an embodiment of the invention, it may be desirable to beable to discount the one of the classifiers, to reflect our lack ofconfidence in its reliability. For example, the second classifier (S2)in the first example (in which the classifiers seemed to agree onselecting the first rate class) may be discounted:I ¹=[0.8, 0.15, 0.05, 0, 0, 0] and I ²=[0.9, 0.05, 0.05, 0, 0, 0]

This discounting is represented by allocating some of the classifier'sweight, in this instance 0.3, to the universe of discourse U, (U=Nodecision: Sent_to_UW):I ¹=[0.8, 0.15, 0.05, 0, 0, 0] and I ²=[0.6, 0.05, 0.05, 0, 0, 0.3]

The results of the fusion of I¹ and I² are summarized in FIG. 18 below.Summarization chart 1800 illustrates the classes 1810, T-norms 1820, thefused intersection results 1830 and the confidence measure 1840. Therate classes have a slightly lower weight (for T3, T2.5, T2), but thenormalized confidence is higher than with respect to FIG. 15, as thereis less conflict. Fusion matrices A are shown in the tables of FIGS.19-23, while the tables of FIGS. 24-28 illustrate matrices Â. Accordingto an embodiment of the invention, a fusion rule based onDempster-Shafer corresponds to the selection of:

a) T-norm operator T(x,y)=x*y; and

b) Penalty function using W=1 (or alternatively d=∞)

Constraint b) implies the penalty matrix P 2900 illustrated in FIG. 29.Therefore, the two additional constraints a) and b) required byDempster-Shafer theory (also referred to as “DS”) imply that theclassifiers to be fused must be uncorrelated (e.g., evidentiallyindependent) and that there is no ordering over the classes, and anykind of disagreement (e.g., weights assigned to elements off the maindiagonal) can only contribute to a measure of conflict and not, at leastto a partial degree, to a measure of confidence. In DS, the measure ofconflict K is the sum of weights assigned to the empty set. Thiscorresponds to the elements with a 0 in the penalty matrix P 2900illustrated in FIG. 29.

According to an embodiment of the invention, the normalized confidence Cdescribed above may be used as a measure of confidence, i.e.:

$\hat{C} = {{{Normalized}\mspace{14mu}{Confidence}\;\left( {\hat{A},P} \right)} = {\sum\limits_{i = 1}^{N + 1}{\sum\limits_{j = 1}^{N + 1}{{\hat{A}\left( {i,j} \right)}*{P\left( {i,j} \right)}}}}}$

The confidence factor Ĉ may be interpreted as the weighted cardinalityof the normalized assignments around the main diagonal, after all theclassifiers have been fused. In the case of DS, the measure ofconfidence Ĉ is the complement (to one) of the measure of conflict K,i.e.: Ĉ=1-K, where K is the sum of weights assigned to the empty set.

An additional feature of the present invention is the identification ofcases that are candidates for a test set, auditing, or standardreference decision process via the comparison module. As illustratedpreviously in FIG. 1, the comparison module has four inputs. Theseinputs include the decision of the production engine, which according toan embodiment of the invention, is one of five possible rate classes ora no-decision (e.g., “send the case to a human underwriter”), i.e.:

D(FLE)=r1 and r1 ∈{Best, Preferred, Select, Standardplus, Standard,Sent_to_UW}

An additional input may comprise the decision of the fusion module,which according to an embodiment of the invention, is also one of fivepossible rate classes or a no-decision (e.g., “send the case to a humanunderwriter”), i.e.: D(FUS)=r2 and r2 ∈{Best, Preferred, Select,Stanardplus, Standard, Sent_to_UW}

An additional input may comprise the degree of confidence in theproduction engine decision. The computation of the confidence measure isdescribed in the U.S. patent application Ser. Nos. 10/173,000 and10/171,575, entitled “A Process/System for Rule-Based InsuranceUnderwriting Suitable for Use by an Automated System,” the contents ofwhich are incorporated herein by reference. This measure may be equatedto the degree of intersection of the soft constraints used by a fuzzylogic engine (“FLE”). This measure may indicate if a case had all itsconstraints fully satisfied (and thus C(FLE)=1) or whether at least oneconstraint was only partially satisfied (and therefore C(FLE)<1).

An additional input may comprise the degree of confidence in the fusionprocess. The normalized confidence measure Ĉ is C(FUS). According to anembodiment of the invention, the first test performed is to compare thetwo decisions, i.e., D(FLE) and D(FUS). FIG. 30 illustrates all thepossible comparisons between the decision of the production engine andthe fusion module. Comparison matrix 3000 illustrates the D(FLE) classes3010 and the D(FUS) classes 3020. From the table it can be seen thatlabel A shows that D(FLE)=D(FUS) and they both indicate the same,specific rate class. Further, label B shows that the fusion module madeno automated decision and suggested to send the application to a humanunderwriter, i.e. D(FUS)=No Decision. Label C shows that D(FLE)≠D(FUS)and that both D(FLE) and D(FUS) indicate a specific, distinct rateclass. In addition, label D shows that D(FLE)≠D(FUS), and in particular,that the FLE made no automated decision and suggested to send theapplication to a human underwriter, while the Fusion module selected aspecific rate class. Label E shows that D(FLE)=(FUS) and that bothD(FLE) and D(FUS) agree not to make any decision.

A second test may be done by using this information in conjunction withthe measures of confidence C(FLE) and C(FUS) associated with the twodecisions. With this information, the performance of the decision enginemay be assessed over time by monitoring the time statistics of theselabels, and the frequencies of cases with a low degree of confidence.According to an embodiment of the invention, a stable or increasingnumber of label A's would be an indicator of good, stable operations. Anincrease in the number of label B's would be an indicator that thefusion module (with its models) needs to be retrained. These cases mightbe shown to a team of senior underwriters for a standard referencedecision. An increase in the frequency of label C's or of cases with lowconfidence could be a leading indicator of increased classification riskand might warrant further scrutiny (e.g., auditing, retraining of thefusion models, re-tuning of the production engine). An increase in labelD's may demonstrate that either the production engine needs re-tuningand/or the fusion modules needs retraining. An increase in label E's maydemonstrate an increase in unusual, more complex cases, possiblyrequiring the scrutiny of senior underwriters. Thus, the candidates forthe auditing process will be the ones exhibiting a low degree ofconfidence (C(FUS)<T1), regardless of their agreement with the FLE andthe ones for which the Fusion and the Production engine disagree, i.e.,the ones labeled C.

The candidates for the standard reference decision process are the casesfor which the fusion module shows no decisions (labeled B or E). Thecandidates to augment the test set may be selected among the cases forwhich the fusion module and the production engine agree (label A). Thesecases may be filtered to remove the cases in which the production enginewas of borderline quality (C(FLE)<T2) and the cases in which theconfidence measure of the fusion was below complete certainty(C(FUS)<T1). Thresholds T1 and T2, may be data dependent and must beobtained empirically. By way of example, T1=0.15 and T2=1. Table 2 belowsummarizes the conditions and the quality assurance actions required,according to an embodiment of the invention. Dashes (“-”) in the entriesof the table may indicate that the result of the confidence measures arenot material to the action taken and/or to the label applied.

TABLE 2 Decisions Confidence Label from Measures Table 7 C(FLE) C(FUS)ACTION A ≧T2 ≧T1 Candidate to be added to data set for tuning of FLE B —— Candidate for Stand Ref Dec. Process. After enough cases arecollected, re-tune the classifiers C — — Candidate for Auditing D — —Candidate for Stand Ref Dec. Process. After enough cases are collected,re-tune the classifiers E — — Candidate for Stand Ref Dec. Process.After enough cases are collected, re-tune the classifiers — — <T3Candidate for Auditing

According to an embodiment of the invention, the fusion module may beimplemented using software code on a processor. By way of an example ofthe results of an implementation of the present invention, a fusionmodule was tested against a case base containing a total of 2,879 cases.After removing 173 UW cases, the remaining 2,706 cases were segmentedinto 831 nicotine users, with three rate classes, and 1,875 non-nicotineusers, with five rate-classes. These cases were then used to test thefusion process. Because the cases for which the production engine hadmade no decision were removed, use of a comparison matrix similar to theone of Table 1400 will only have labels A, B, C. The fusion wasperformed using the T-norm T2(x,y)=x*y.

FIG. 31 illustrates the effect of changing the threshold T1 on themeasure of confidence Ĉ, were 0<Ĉ≦1. Table 3100 display decisions 3110,confidence thresholds 3120 and the case distributions 3130 based on theconfidence threshold 3120. Each column shows the number of cases whosemeasure of confidence Ĉ is >T1. As the threshold is raised, the numberof “No Fusion Decision” increases. A “No Fusion Decision” occurs whenthe results of the fusion are deemed too weak to be used. When thethreshold T is 1, no case is rejected on the basis of the measure ofconflict. This leaves 36 cases for which no decision could be made. Asthe threshold is decreased, decisions with a high degree of conflict arerejected, and the number of “No Fusion Decisions” increases.

“Agreements” occur when the fused decision agrees with the FLE and withthe Standard Reference Decision (SRD). “False Positives” occur when thefused decision disagrees with the FLE, which in turn is correct sincethe FLE agrees with the Standard Reference Decision (“SRD”). “FalseNegatives” occur when the fused decision agrees with the FLE, but boththe fusion decision and the FLE are wrong, as they disagree with theSRD. “Corrections” occur when the fused decision agrees with the SRD anddisagrees with the FLE. Finally, “Complete Disagreement” occurs when thefused decision disagrees with the FLE, and both the fused decision andthe FLE disagree with the SRD. Further, similar results were obtainedfor nicotine users, and these results are illustrated in FIG. 32, withtable 3200 displaying decisions 3210, confidence thresholds 3220 and thecase distributions 3230 based on the confidence thresholds 3220.

FIG. 33 illustrates a Venn diagram 3300 illustrating the situation forthe threshold T1=0.15 (i.e., for C≧0.15) for the non-nicotine users,while FIG. 34 illustrates a Venn diagram 3400 illustrating the situationfor the threshold T1=0.15 (i.e., for C≧0.15) for the nicotine users. Inthe case of the non-nicotine users (for T1=0.15) the following labelsresult:

A: 1,588+27=1,615 (86.13%) in which 3310 D(FUS)=D(FLE); (e.g.,agreements 3310 and false negative 3320)

B:=36 (1.92%) in which the fusion did not make any decision (from Ĉ=0);

C1: 212−36=176 (9.39%) in which the fusion was too conflictive (Ĉ<0.15);and

C2: 22+25+1=48 (2.56%) in which D(FUS)≠D(FLE) (e.g., false positive3330, corrections 3340 and complete disagreements 3350).

In the case of the nicotine users (for T1=0.15), the following labelsresult:

A: 729+15=744 cases (89.5%) in which D(FUS)=D(FLE); (e.g., agreements3410 and false negatives 3420);

B:=37 cases (4.5%) in which the fusion did not make any decision (fromĈ=0);

C1: 68−37=31 cases (3.7%) in which the fusion was too conflictive(Ĉ<0.15); and

C2: 16+3=19 cases (2.3%) in which D(FUS)≠D(FLE) (e.g., false positives3430, corrections 3440 and complete disagreements 3450).

According to the present example, since there is no SRD in production,there can only be reliance on the degree of conflict and the agreementbetween the fused decision and the FLE. If the disagreement between FLEand FUS (e.g., subset C2) is used, it can be observed that the number ofcases in which the fusion will disagree with the FLE, and make aclassification, is 48/1875 (2.56%) for non-nicotine users and 19/831(2.3%) for nicotine users. This may be considered a manageablepercentage of cases to audit. Further, this sample of cases may beaugmented by additional cases sampled from subsets C1.

A further analysis of set C2 in the case of non-nicotine users showsthat out of 48 cases, the fusion module called 22 of them correctly and26 of them incorrectly. From the 26 incorrectly called cases, 14 caseswere borderline cases according to the FLE. This illustrates that theproblematic cases may be correctly identified and are good candidatesfor an audit.

A further analysis of set C2 in the case of nicotine users shows thatout of 19 cases, the fusion module incorrectly called 16. Of these 16cases, 6 cases were borderline cases, i.e., the FLE only had partialdegree of satisfaction of the intersection of all the constraints e.g.,C(FLE)<0.9. Furthermore, 11 cases had a conflict measure Ĉ<0.4. If theunion of these two subsets (e.g., the borderline cases and the conflictmeasure cases) is taken, the results are 13 cases that are eitherborderline (from the FLE) or have low confidence in the fusion, and theremaining 3 cases were ones that the CBE could not classify (i.e., itcould not find enough similar cases). This again demonstrates that theproblematic cases may be generally correctly identified and are worthauditing.

The set B (4.5%) illustrates a lack of commitment and is a candidate fora review to assign an SRD. The set A may be a starting point to identifythe cases that could go to the test set. However, set A may need furtherfiltering by removing all cases that were borderline according to theFLE (i.e., C(FLE)<T2), as well as removing those cases whose fusionconfidence was too low (i.e., C(FUS)<1). Again T2 will be determinedempirically, from the data.

Various aspects of the fusion module will now be discussed in greaterdetail below. It is understood that various portions of the fusionmodule, as well the different aspects described below, may be performedin different manners without departing from the scope of the invention.

2. Outlier Detector

One component of a fusion module may be determining outlierapplications. According to an embodiment of the invention, it may bedesirable to detect all classification assignments to applications, suchas insurance applications, that are inconsistent and thereforepotentially incorrect. Applications that are assigned these inconsistentlabels may be defined as outliers. The concept of outliers may extendbeyond the realm of insurance underwriting and be intrinsic to all riskclassification processes, of which the determination of the properpremium to cover a given risk (i.e., insurance underwriting) is just anexample. Therefore, the ultimate domain of this invention may beconsidered risk classification, with a focus on insurance underwriting.

According to an embodiment of the invention, the existing risk structureof the risk classification problem is exploited from the riskassignments made by the underwriters, similar to the dominance-basedclassifier described in greater detail below. But whereas the dominancebased classifier uses the risk structure to produce a risk assignmentfor an unlabeled application, the outlier detector examines the riskstructure to find any applications that might have been potentiallyassigned an incorrect risk assignment by the underwriter.

The outlier detector may add to the rationality of the overallunderwriting process by detecting globally inconsistent labels andbringing it to the attention of human experts. Many papers in thedecision sciences demonstrate that in the presence of informationoverload, humans tend to be boundedly rational and often,unintentionally, violate compelling principles of rationality likedominance and transitivity. The outlier detector may attempt to counterthese drawbacks exhibited by human decision-makers and make thedecision-making process more rational. As a result, the risk assignmentscan be expected to be more optimal and consistent.

Further, by bringing these globally inconsistent risk assignments to theattention of the underwriters, the system may gain knowledge aboutexceptional decision rules, or additional features that are implicitlyused by experts and which may be left unmentioned during the initialdesign stages of an automated system. This additional knowledge may beused to improve the performance of any automated system. Thus, theoutlier detector may also act as a knowledge-eliciting module.

By removing globally inconsistent risk assignments from the initial set,the detection of outliers may further improve the performance andsimplicity of other supervised classification systems, such as neuralnetworks and decision-tree classifiers when used as the primaryautomated system. This is because the presence of global inconsistenciesmay add to the “non separability” of the feature space, which will oftenlead to either inferior learning, or very complicated architectures. Asthe outlier detector reduces the number of global inconsistencies, acleaner, more consistent training set may be expected to result in abetter learning, and by a simpler system. Hence, the outlier detectormay improve the classification accuracy, and simplicity of otherautomated systems.

Because the outlier detector uses the principle of dominance to capturethe risk structure of the problem, the outlier detector has explanationcapability to account for its results. This is because dominance is acompelling principle of rationality and thus the outliers detected bythe system are rationally defensible.

According to an embodiment of the invention, the functionality of theoutlier detection system may be generic, so that it can be used todetect outliers for any preference-based problem where the candidates inquestion are assigned preferences based on the values that they takealong a common set of features, and the preference of a candidate is amonotonic function of its feature-values. Therefore, the applicabilityof an outlier detection system transcends the problem of insuranceunderwriting, and can be easily extended to any risk classificationprocess.

In many domains where expert opinions are used to score entities, theset of entities that have already been scored are stored as precedents,cases, or reference data points for use in future scoring or comparisonwith new candidates. The outlier detector can help in ensuring that anynew candidate case that goes into the reference dataset will always leadto a globally consistent dataset, thereby ensuring that the referencedataset is more reliable.

According to an embodiment of the invention, an outlier detector mayexploit the existing risk structure of a decision problem to discoverrisk assignments that are globally inconsistent. The technique may workon a set of candidates for which risk categories have already beenassigned (e.g., in the case of insurance underwriting, for example, thiswould pertain to the premium class assigned to an application). For thisset of labeled candidates, the system may find all such pairs ofapplications belonging to different risk categories, which violate theprinciple of dominance. The outlier detector attempts to match the riskordering of the applications with the ordering imposed by dominance, anduse any mismatch during this process to identify applications that werepotentially assigned incorrect risk categories.

As described previously, automating an insurance underwriting processmay involve trying to emulate the reasoning used by the human expertwhile assigning premium classes to insurance applications, and findingcomputable functions that capture those reasoning principles. Accordingto an embodiment of the invention, the risk category of an applicationdepends upon the values taken by the application along variousdimensions, such as Body Mass Index (“BMI”), Cholesterol Level, andSmoking History. The values of the dimensions are then used to assignrisk categories to insurance applications. An automated system wouldoperate on these same features while trying to emulate the underwriter.Typically, the risk associated with an application changes with changesto the magnitude of the individual features. For example, assuming thatall other features remaining the same, if the BMI of an applicantincreases, the application becomes riskier. The outlier detector usesthis knowledge to detect all such applications that do not satisfy theprinciple of dominance.

According to an embodiment of the invention, there is a monotonicnon-decreasing relationship between all the feature-values and theassociated risk (e.g., higher values imply equal-or-higher risk).Variables that do not meet this relationship may be substituted by theirmirror image, which will then satisfy this condition. For instance, letus assume that the relevant medical information for a non-smokerapplicant is captured by the following five variables:

-   -   X1 =Cholesterol,    -   X2 =Cholesterol Level,    -   X3 =Systolic Blood Pressure,    -   X4 =Diastolic Blood Pressure,    -   X5 =Years since quitting smoking (if applicable).

Mortality risk is monotonically non-deceasing with respect to the firstfour variables, meaning that such risk can increase (or remain the same)as the values of the four variables increase. However, higher values inthe fifth variable have a positive effect, as they decrease themortality risk. Therefore, the fifth variable needs to be transformedinto another variable. By way of example, X5 may be transformed intoX5′, where X5′ is defined as X5′=K−X5=K-“years since quitting smoking”.K is a constant, e.g., K=7, so that higher values of X′ will reflectsame or increased mortality risk. Other relationships between all thefeature-values may also be used.

Further, if two insurance applicants A and B are compared whereapplicants A and B are identical along all features, except that theapplicant B has a higher BMI than A, then the risk associated withapplicant A cannot be greater than that associated with B. In otherwords, the premium associated to the rate class assigned to A should notbe higher than that one assigned to B. The above reasoning principle isreferred to, in decision theory, as the principle of dominance and inthe above example applicant A dominates applicant B. The terminologydominates(A,B) is used to capture this relation between applicant A andapplicant B.

For example, given two applications A and B, it can be said thatapplication A dominates application B if and only if application A is atleast as good as application B along all the features and there is atleast one feature along which application A is strictly better thanapplication B. The dominates relation may be based on the abovedefinition of dominance. It is a trichotomous relation, meaning thatgiven two applications A and B either application A dominatesapplication B, application B dominates application A, or neitherdominates the other. In the case where neither applicant dominates theother, each application may be better than its counterpart alongdifferent features. In such a case, application A and application B maybe said to be dominance-tied. For example, as illustrated in Table 3below, assume there are three applicants A, B, and C with the followingfeature values:

TABLE 3 Application BMI Cholesterol BP_sys A 25 255 115 B 26 248 120 C24 248 112

Assuming for simplicity that these are the only three features used toassess the risk of an applicant. By the definition, it can be seen thatapplication C dominates both application A and application B, sinceapplication C is at least as good (e.g., as low) as application A andapplication B along each feature, and moreover there is at least onefeature along which application C is strictly better (e.g., strictlylower) than both application A and application B. However, application Aand application B are dominance-tied since each is better (e.g., lower)than the other along some feature (application A has better cholesterolvalue while application B has better BMI value).

According to an embodiment of the invention, the relationNo_Riskier_Than(A,B) is true if the risk associated with applicant A(say r_(A)) is no higher than that associated with applicant B (sayr_(B)), i.e.,No_Riskier_Than(A,B)(r_(A)≦r_(B)).

According to an embodiment of the invention, based on the assumptionthat the risk associated with an applicant is a monotonic non-decreasingfunction of the feature values, it can be seen that for any pair ofinsurance applications, if the dominates relation holds between the twoapplications in a certain direction (e.g., application A dominatesapplication B), then the No_Riskier_Than relation will also hold in thesame direction (e.g., application A is No_Riskier_Than application B).In other words, the dominates relation is a sufficiency condition forthe No_Riskier_Than relation. That is:dominates(A,B)→No_Riskier_Than(A,B).

An application may be considered an outlier based on one or morecharacteristics. According to an embodiment of the invention,application X and application Y are marked as outliers if application Xdominates application Y, and application X is assigned a risk categorythat associates greater risk with application X compared to applicationY. According to an embodiment of the invention, application X andapplication Y are marked as outliers if application Y dominatesapplication X, and application Y is assigned a risk category thatassociates greater risk with application Y compared to application X.

The above statements can be described formally with the followingequation:(X,Y are outliers)(dominates(X,Y)Λ(r_(X)>r_(Y)))v( dominates(Y,X)Λ(r_(Y)>r_(X)))

As can be seen, from the definitions of the dominates relation and theNo_Riskier_Than relation, inconsistent risk assignments may beidentified. If application X dominates application Y, then application Xwill be at least as good as application Y along all features andstrictly better than application Y along at least one feature. As aresult, logically, application X cannot be riskier than application Y.Therefore, if the risk assignments made by the underwriters are suchthat application X is categorized as being riskier than application Y,then the existing risk assignments made to application X, andapplication Y, or to both application X and application Y, may likely belogically infeasible. Therefore, both application X and application Yare labeled as outliers, e.g., applications that have inconsistentassignments, and therefore potentially incorrect risk categories.According to an embodiment of the invention, in order to exploit thepresence of the dominance relation between two applications and tologically restrict the risk assignment of the two applications, it maybe necessary to ensure that all the features that are being used by theexperts during the risk assignments are also used during the dominancecomparisons.

The steps involved in outlier detection according to an embodiment ofthe invention are described below and shown in FIG. 35. An outliermodule operates on a set A of applications, each of which has beenassigned a risk category from one of the i possible categories. Thesystem may be thought of as operating on a set of tuples {(A_(j),x)}where x is the risk category assigned by the underwriter to applicationA_(j). The process for outlier detection may be implemented inpseudocode as set forth below:

Outlier_detect(A:{A_(j),x}) { for each tuple (A_(j),x)∈ A {   for eachtuple (A_(k),y) ∈ A where r_(y)>r_(x)   {     if(dominates(A_(k),A_(j)))       mark A_(j), A_(k) as outliers;      break;     else       next A_(k);   } next A_(j); } Report set ofoutliers; }

As defined earlier, outliers are pairs of tuples (A_(p),x), (A_(q),y)where A_(p) dominates A_(q) but r_(y)<r₁. FIG. 35 illustrates aflowchart for detecting outliers given a set of labeled applications. Atstep 3510, a tuple (A_(i),x) is identified. A tuple (A_(j),y) isidentified at step 3520, where the rate class r_(y) for tuple (A_(j),y)is greater than the rate class r_(x). At step 3530, a determination ismade whether tuple (A_(j),y) dominates tuple (A_(i),x) (e.g., Dominates((A_(j),i)). If yes, tuples (A_(j),x) and (A_(j),y) are marked asoutliers. The system then determines at step 3550 if there is anothertuple (A_(j),y), where r_(y)>r₁. This determination is also made iftuple (A_(j),y) does not dominate tuple (A_(i),x). At step 3550, ifthere is another (A_(j),y), where r_(y)>r_(x), the process returns tostep 3520. If there is no other tuple (A_(j),y) where r_(y)>r₁, adetermination is made at step 3560 whether there is another tuple(A_(i),x). If yes, the process returns to step 3510, while if not, thesystem ends at 3570.

According to an embodiment of the invention, an outlier detector may beimplemented in software code, and tested against a database of cases.For example, an outlier detector may be tested against a database ofapproximately 2,900 cases. In such an example, the outlier detectoridentified more than a dozen of subsets containing at least oneinconsistency. The results produced by the outlier detector in thisexample are shown in Table 4 below, along with a few relevant featurevalues.

TABLE 4 Cho- Curr les- Smok- Risk ter- ing Class Age Height WeightBP_Sys BP_Dias ol Chol_Ratio SGOT SGPT GGT Status Build Fam_HistFam_Death PREF 53 62 146 112 80 258 4.1 21 16 17 0 26.70 0 0 BEST 29 77229 132 84 278 4.6 25 22 17 0 27.16 0 0

In Table 4 above, each row represents an insurance application for whichthe risk classification had already been determined, as shown in thefirst column. The risk class “BEST” is a lower risk class compared tothe risk class “PREF.” A person classified in the “BEST” risk class willhave to pay a lower premium than a person classified in the “PREF”class. Yet, it can be seen that the application indicated in the rowfirst of Table 4 dominates the application of the second row. In thepresent example, upon sending these two applications to humanunderwriters for reconsideration, the risk classifications for theapplications were reversed. This simple example illustrates the use ofan outlier detector to obtain more consistent risk assignments.

As illustrated in FIG. 1 above, outlier detector 180 is shown after thefusion to insure that any new addition to the best-cases database wouldbe dominance-consistent with the existing cases. Another potential usefor the outlier detector is its application to the training-casesdatabase used to train each of the decision engines used by the fusionmodule. This is a Quality Assurance step for the training data to insurethat the training cases do not contain outliers (e.g., inconsistentcases in the dominance sense) so as to improve the learning phase of thefour models illustrated (CBR, NN, MARS, Dominance) before they are usedas run-time classifiers for the Quality Assurance process of theproduction engine. According to an embodiment of the invention, asillustrated in FIG. 36, an outlier detector 3610 and a trainingcase-base 3620 may be positioned for quality assurance for CBR DE 3630,MARS DE 3640, NN DE 3650 and DOM DE 3660, the output of which is fedinto a fusion module (not shown).

3. Dominance Classifier

According to an embodiment of the invention, the risk structure of anunderlying problem may also be exploited to produce a risk categorylabel for a given application, such as an insurance application. Thisrisk classification can be assured to be accurate with a high degree ofconfidence. Specifically, as described above in relation to the outlierdetector, the application of a dominance classifier may also providerisk assignments having a high confidence measure. Further, when strictdefinitions are implemented, the relative accuracy of the systemapproaches 100%, thus minimizing the degree of mismatch between the riskassignment made by a human underwriter and the automated rate classdecisions.

A dominance classifier may have many of the advantages of the outlierdetector. The principle of dominance is a compelling principle ofrationality and thus the classification produced by the technique isrationally defensible. This imparts explanation capability to theclassification making it transparent and easy to comprehend. Further,there are no iterative runs involved in tuning. As a result, the tuningprocess may reduce and become less time-consuming. The output of thisdominance-based classifier can be combined in a fusion module with theoutput(s) generated by other classifiers. A fusion process may be usedfor quality assurance of a production decision engine, to provide astronger degree of confidence in the decision of the engine, in the caseof consensus among the classifiers, or to suggest manual audit of theapplication, in the case of dissent among the classifiers.

According to an embodiment of the invention, automating an insuranceapplication underwriting process may essentially involve trying toemulate the reasoning used by a human expert while assigning premiumclasses to insurance applications, and finding computable functions thatcapture those reasoning principles. The risk category of an applicationdepends upon the values taken by the application along variousdimensions, such as, but not limited to, body mass index (BMI),cholesterol level, and smoking history. An underwriter makes use ofthese values to assign risk categories to the applications. Hence, anautomated system should operate on these same features while trying toemulate the underwriter. Typically, the manner in which the riskassociated with an insurance application changes with changes to themagnitude of the individual features is also known. For example, whenall other features in an insurance application remain the same, if theBMI of an applicant increases, the application becomes riskier.

A dominance-based risk classification may use this knowledge to generatea risk category for a given application, such as an insuranceapplication. According to an embodiment of the invention, an assumptionmay be made that there is a monotonic non-decreasing relationshipbetween all the feature-values and the associated risk (i.e., highervalues imply equal-or-higher risk). For those variables that do not meetthis relationship, a mirror image may be substituted, which will thensatisfy this condition that lower values correspond to lower risk. Thiscan be seen with reference to Table 3 regarding the outlier detector.

Further, as discussed above with respect to the outlier detector, therelation:dominates (A,B)→No_Riskier Than (A,B) still holds

The term Bounded_within(B,[A,C]) may be used when application B isbounded_within application A and application C, if and only ifapplication A dominates application B and application B dominatesapplication C, i.e.,Bounded_within(B,{A,CJ}) dominates(A, B) Λdominates(B, C).

This relation may then be read as “B is bounded within A and C.”

If application B is bounded within two applications A and C, and if therisk category assigned to applications A and C is the same, then therisk category of application B has to be the same as that ofapplications A and C. i.e.,Bounded_within(B, [A, C])Λ(r_(A)=r_(C)=r)→(r_(B)=r)

To better demonstrate this, suppose the following is present:Bounded_within(B, {A, C})Λ(r_(A)=r_(C)=r).

This implies thatdominates(A, B)Λdominates(B, C)Λ(r_(A)=r_(C)=r).Or,No_Riskier_Than(A,B)ΛNo_Riskier_Than(B,C)Λ(r_(A)=r_(C)=r).

Based on the definitions of the relation, the above can be rewritten as,(r_(A)≦r_(B))Λ(r_(B)≧r_(C))Λ(r_(A)=r_(C)=r).

In other words,r_(B)=r

thereby demonstrating the principle of dominance based riskclassification.

This principle may serve as the basis for a risk classification. For anygiven application B with unassigned risk category, a determination ismade whether there exist two applications A and C such that the LeftHand Side (LHS) of the principle is satisfied, i.e.,Bounded_within(B,{A,C})Λ(r_(A)=r_(C)=r). If this occurs, the riskcategory of application B is assigned to be the same as that ofapplications A and C.

Even if an application A dominates another application B, the twoapplications may still be quite close in terms of their feature-valuesso that they belong to the same risk category. In other words, it may beexpected for the dominates relation to hold between some pairs ofapplications even if the two applications belong to the same riskcategory. This may mean that further partitions of the applicationswithin a risk category may be made, such as into the best, non-dominatedsubset and worst, non-dominating subset.

According to an embodiment of the invention, the best, non-dominatedsubset for a given risk category may be defined as the one that containsall such applications that are not dominated by another applicationwithin that risk category. This may also be referred to as thePareto-best subset.

According to an embodiment of the invention, the worst, non-dominatingsubset for a given risk category may be defined as the one that containsall those applications that do not dominate even a single application inthat risk category. This may also be referred to as the Pareto-worstsubset.

To visualize these two subsets geometrically, FIG. 37 may be referredto, which shows a plot of features f1 3710 and f2 3720 for 1000insurance applications. The insurance applications are plotted as pointsin the 2-dimensional feature space. For simplicity, assume that theseare the only two features used while assigning a risk category to theapplications, and that the lower values along a feature correspond to alower risk. In FIG. 37, circles denote the Pareto-best subset 3730 whilethe squares denote the Pareto-worst subset 3740. The circles take thelowest (e.g., the most desirable) values along both features while thesquares take on the highest (e.g., the least desirable) values. Inaddition, using the definition of the Pareto-best subsets 3730 and thePareto-worst subsets 3740 as set forth above, each of the remaininginsurance applications is such that at least one application representedby a circle dominates it, and it dominates at least one applicationrepresented by a square. In other words, for each point X that is not inthe Pareto-best subset(O) 3730 or in the Pareto-worst subset(P) 3740 inFIG. 37, there is at least one square S and one circle C such thatBounded_within(X,{C,S}) is true. For example, suppose that every circleand square in FIG. 37 representing an application was assigned the samerisk category r. Then, by applying the principle of dominance-based riskclassification, all the points shown in FIG. 37 can be assigned the riskcategory r as well.

According to an embodiment of the invention, the production of the twosubsets O and P is identical to the production of the dominance subsetin discrete alternative decision problems. By way of example, articlesby Kung, Luccio, and Preparata (1975), and Calpine and Golding (1976),the contents of which are incorporated herein by reference, presentalgorithms which can create these subsets in O(n.log^(m−1)(n)) time,where n is the number of candidates involved and m is the number offeatures along which the dominance comparisons are being done. Hence,for an underwriting problem with r risk categories, there may be 2r suchsubsets, or one pair for each risk category representing the risksurfaces that form the upper bound and the lower bound.

According to an embodiment of the invention, an algorithm may producethe Dominance subset for a given set of alternatives X(n,m) where n isthe number of candidates and m is the number of features used. The termDominance(X,k) may be used to indicate the application of such analgorithm to the set X(n,m), where k is either +1 or −1, depending uponwhether higher or lower feature values are desired to be considered asbetter during dominance comparisons. According to an embodiment of theinvention, two principal modules, the tuning module and theclassification module, may be used. The tuning module may compute thePareto-best and Pareto-worst subsets for each risk category. TheClassification module may use the results of the tuning to classify newapplications.

The tuning module may use the Dominance algorithm to compute thePareto-best and the Pareto-worst sets for each risk category. Given aset of applications A, such as insurance applications that have beenpartitioned into i different risk categories by the underwriter, tuningmay use the pseudocode set forth below:

TUNE(A,i){ for each risk category r_(i)   {   Compute and store theindices of the Pareto-Best subset O(r_(i)).     Obtain the Dominance(A)enforcing that lower feature-     values are better.   Compute and storethe indices of the Pareto-Worst subset P(r_(i)).     Obtain theDominance(A) enforcing that higher feature-     values are better.   }

FIG. 38 is a flowchart illustrating the steps involved in the tuningprocess according to an embodiment of the invention. At step 3800, eachseparate risk category is determined. At step 3802, a set ofapplications A is divided into the different risk categories. At step3804, the Pareto-best subset of the applications within each riskcategory is computed. At step 3806, the Pareto-best subset is stored. Atstep 3808, the Pareto-worst subset of the applications within each riskcategory is computed. At step 3810, the Pareto-worst subset is stored,completing the tuning process at step 3812.

The classification module may use the sets O and P from the tuningprocess to assign risk classifications to new applications. According toan embodiment of the invention, the classification module assigns a riskcategory to any new application by checking if a given applicationsatisfies the Bounded_within relation with respect to a Pareto-best, andanother Pareto-worst application for a given rate class. According to anembodiment of the invention, given a set of unlabeled applications, U,and the Pareto-best subsets and the Pareto-worst subsets obtained foreach of the i risk categories from tuning, each application in U isassigned a risk category. Assignment of a risk category may be carriedout according to the pseudocode set forth below using the principle ofdominance based risk classification:

FIG. 39 illustrates the steps involved in the classification processaccording to an embodiment of the invention. At step 3902, anapplication is selected from U. At step 3904, a risk category r_(k) isselected. At step 3906, a determination is made whether application Z isbounded within some x∈O(r_(k)), y∈P(r_(k)). If not, a determination ismade if there is another risk category r_(k), at step 3908. If there isanother r_(k), the process returns to step 3904. If there is no otherr_(k), application Z is declared unresolved at step 3910, and adetermination is made if there is another application Z at step 3912. Ifthere is another application Z, the process returns to step 3902. Ifthere is no other application Z, the process ends at step 3916.

Returning to step 3906, if application Z is bounded, risk category r_(k)is assigned to application Z at step 3914. The process then moves on tostep 3912 to determine if there is another application Z.

When assigning a risk category, such as according to the pseudocodesteps illustrated previously or according to the steps of FIG. 39, theremay be situations that need to be accounted for in the above riskassignment algorithm. One example is where there is no risk category forwhich the Bounded_within condition is satisfied for A[j]. Anotherexample is where there are at least two risk categories for which theBounded_within condition is satisfied for A[j]. Each of the above twosituations can lead to a different kind of ambiguity. Other situationsmay also lead to various types of ambiguity.

According to an embodiment of the invention, where there is no riskcategory for which the Bounded_within condition is satisfied for A[j],an application may be regarded as ambiguous by the system. No riskcategory is assigned to the application and the application is marked asunresolved.

The comparison matrix 4000 illustrated in FIG. 40 provides an example ofthe performance of the system for a particular set of applicants. In theexample illustrated in FIG. 40, the system initially used the tuning setin order to compute the Pareto-best and the Pareto-worst subsets foreach of the risk categories, which in this case are eight riskcategories. The system may then classify a set of applications that werenot in the tuning set. For these applications, risk assignments werealso obtained from the human underwriters. This allows a comparison ofthe performance of the system with that of the experts using thecomparison matrix.

As mentioned earlier, an application that does not satisfy theBounded_within relation for any of the risk categories, is marked asunresolved by the system. These applications are shown in the column4002 labeled “UW.” As can be seen, quite a large number of applicantswere marked as unresolved by the system. However, for the applicationsthat were assigned a risk category by the system, the system wasaccurate 100% of the time. Thus, 52 applications were correctlyclassified in column 4004 labeled “PB,” 22 applications were correctlyclassified in column 4006 labeled “P,” 16 applications were correctlyclassified in column 4008 labeled “Sel,” 10 applications were correctlyclassified in column 4010 labeled “Std+,” 3 applications were correctlyclassified in column 4012 labeled “Std,” 28 applications were correctlyclassified in column 4014 labeled “P Nic,” 8 applications were correctlyclassified in column 4016 labeled “Std+Nic,” and 3 applications werecorrectly classified in column 4018 labeled “Std Nic.” Hence, theprinciple of dominance based risk classification presented in thisletter has the potential to produce risk assignments with a high degreeof confidence. For the few applications that are misclassified above,the use of another system called the dominance based outlier detectionsystem may be used. The dominance based outlier detection system hasbeen described above.

As can be seen from the example of FIG. 40, the classifier is 100%accurate, but may have a lower coverage, meaning that it does notprovide a decision for a large number of cases. A different tradeoff maybe achieved between relative accuracy and coverage of the system byallowing a minor relaxation of the classification rule used in theextreme rate classes (e.g., the best and worst rate class). According toan embodiment of the invention, one type of modification makes use ofthe fact that since the risk categories are totally ordered, theprinciple of dominance-based risk classification can be relaxed for thebest and the worst risk categories. This relaxation may therefore beexpected to improve the coverage of the automated system. The basis forthis relaxation principle may be seen from understanding that if theapplication for applicant X dominates the application for applicant Asuch that the risk category assigned to application A is the best riskcategory for the problem, say r_(best), then the risk category ofapplication X is also r_(best), i.e.:

ti dominates(X,A)Λ(r_(A)=r_(best))→(r_(x)=r_(best)).

For example, assume that there is an application X such that itdominates application A, where it is known that A is assigned the bestrisk category, i.e.:r_(A)=r_(best)

Since application A belongs to the best risk category, no otherapplicant can be assigned a better risk category than application A. Inother words,rX ≧rA

However since application X also dominates application A, application Xcan be no riskier than application A which implies that:rX ≦rA

From this, it can therefore be inferred that:rX=rbest

thereby demonstrating the applicability of the relaxation conditiondescribed above with respect to the best classification. Further, therelaxed principle of dominance based risk classification for the worstrisk category can be seen by noting that if application A dominatesapplication X such that the risk category assigned to application A isthe worst risk category, say r_(worst), then the risk category ofapplication X is also r_(worst); i.e.:dominates(A,X)Λ(r_(A)=r_(worst))→(r_(x)=r_(worst)).

For example, assume that there is an application X such that it isdominated by application A, where it is known that A is assigned theworst risk category. i.e.:r_(A)=r_(worst)

Because application A belongs to the worst risk category, every otherapplicant belongs to a risk category that is better than or equal tothat of application A. In other words:r_(X)≦r_(A)

However, since application A also dominates application X, thereforeapplication A must also be no riskier than application X, which impliesthat:r_(X)≧r_(A)

From this, it is demonstrated that:r_(x)=r_(worst)thereby demonstrating the applicability of the relaxation conditiondescribed above with respect to the worst classification. Thus,according to an embodiment of the invention, the steps forclassification remain the same except that during the r_(k)-loop in FIG.39, the application at hand is tested for the relaxed conditionsdescribed above respectively, and assigned the risk category accordinglyif one of the conditions is satisfied.

The comparison matrix 4100 shown in FIG. 41 illustrates performance ofthe dominance based risk classifier used after incorporating the relaxedconditions defined above, during classification of an applicant andtested against a case base of approximately 541 cases. Coverage of theclassifier has improved, since 68 applicants that were initially markedas unresolved by the classifier are now assigned a risk category.Whereas the relative accuracy of the new classifier is not 100% like itscounterpart, the number of misclassifications is relatively few. Inother words, for a large gain in coverage the overall drop in accuracyobtained by the use of the modified classifier may be relatively minor.Thus, the relaxation conditions may permit a tradeoff between accuracyand coverage of the dominance based risk classifier. Where the relativeaccuracy is more important for a problem, the earlier version of theclassifier may be used. On the other hand, if some problem requires thatmore applicants be assigned a risk category, then it may be moredesirable to use the modified classifier. This imparts flexibility tothe system on the whole since it can cater to varying requirements ofaccuracy and coverage from the automated system, which is an addedadvantage of the system.

4. Multivariate Adaptive Regression Splines

According to an embodiment of the invention, a network of multivariateadaptive regression splines (“MARS”) based regression models may be usedto automate decisions in business, commercial, or manufacturing process.Specifically, such a method and system may be used to automate theprocess of underwriting an application as applicable to the insurancebusiness.

According to an embodiment of the invention, a MARS based system may beused as an alternative to a rules-based engine (“RBE”). U.S. patentapplication Ser. No. 10/173,000, filed on Jun. 18, 2002, and U.S. patentapplication Ser. No. 10/171,575, filed on Jun. 17, 2002, titled “AMethod/System of Insurance Underwriting Suitable for Use By An AutomatedSystem,” the contents of which are incorporated herein by reference intheir entirety, describe a fuzzy rule-based system. A MARS model may notbe as transparent as other decision engines (e.g., “RBE”), but mayachieve better accuracy. Therefore, MARS may be used as an alternativeapproach for a quality assurance tool to monitor the accuracy of theproduction decision engine, and flag possible borderline cases forauditing and quality assurance analysis. Further, a MARS module may be aregression-based decision system, which may provide the simplicity ofimplementation of the model since it is based on a mathematical equationthat can be efficiently computed.

According to an embodiment of the invention, a MARS module mayfacilitate the automation of the “clean case” (e.g., those cases with nomedical complications) underwriting decision process for insuranceproducts. A MARS module may be used for other applications as well. AMARS module may be used to achieve a high degree of accuracy to minimizemismatches in rate class assignment between that of an expert humanunderwriter and the automated system. Further, the development of aparallel network of MARS models may use a set of MARS models as aclassifier in a multi-class problem.

The MARS module is described in the context of a method and system forautomating the decision-making process used in underwriting of insuranceapplications. However, it is understood that the method and system maybe broadly applicable to diverse decision-making applications inbusiness, commercial, and manufacturing processes. Specifically, astructured methodology based on a multi-model parallel network of MARSmodels may be used to identify the relevant set of variables and theirparameters, and build a framework capable of providing automateddecisions. The parameters of the MARS-based decision system areestimated from a database consisting of a set of applications withreference decisions against each application. Cross-validation anddevelopment/hold-out may be used in combination with re-samplingtechniques to build a robust set of models that minimize the errorbetween the automated system's decision and the expert humanunderwriter. Furthermore, this model building methodology may be usedperiodically to update and maintain the family of models, if required,to assure that the family of models is current.

FIG. 42 is a flowchart illustrating a process for building a MARS moduleaccording to an embodiment of the invention. At step 4205, one or moreapplications (also referred to as cases) are digitized. Digitization mayinclude assuring that the key application fields required by the modelto make a decision are captured in digital form by data entry.

In step 4210, a case base is formed. Creating a case base may includeassuring that the records corresponding to each application (e.g., case)are stored in a Case Base (CB) to be used for model construction,testing, and validation. In step 4215, preprocessing of cases occurs.Preprocessing may include one or more sub-steps. By way of example,preprocessing may involve location translation and truncation 4216, suchas focusing on values of interest for each field. Further, preprocessingmay involve range normalization 4217, such as normalizing values toallow for comparison along several fields. Preprocessing may alsoinvolve tag encoding 4218, where tag encoding includes augmenting arecord with an indicator, which embodies domain-knowledge in the recordby evaluating coarse constraints into the record itself.

In step 4220, partitioning and re-sampling occurs. According to anembodiment of the invention, five-fold partitioning may be used, with astratified sampling within each rate class used to create five disjointpartitions in the CB. In step 4225, generation of a development andvalidation set occurs. Each partition may be used once as a validationset, with the remaining four used as training sets. This may occur fivetimes to achieve reliable statistics on the model performance androbustness.

At step 4230, one or more model building experiments occur. Experimentswith modeling may involve modeling techniques such as global regressionand classification and regression trees (“CART”) to determine rateclasses from a case description. This may result with the selection ofMARS as the modeling paradigm.

At step 4235, a parallel network of MARS models is implemented.According to an embodiment of the invention, implementation of networksof MARS models may be used to improve classification accuracy.

According to an embodiment of the invention, the MARS model(s) describedmay be used as an input to a fusion module. Fusion of multipleclassifiers based on MARS, Case-based Reasoning, Neural Networks, etc.,may be used to improve classification reliability, as described above.The steps of the process illustrated in FIG. 42 will now be described ingreater detail.

At step 4205, cases are digitized and at step 4210, a case base isformed. According to an embodiment of the invention, a MARS modelframework starts from a database of applications with the correspondingresponse variable (e.g., rate class decisions) provided for each. Thismay be done via cooperative case evaluation sessions with experiencedunderwriters, or may be accomplished via the reuse of previouslycertified cases. This database of applications is hereby referred to asa “Certified Case Base” or a “Case Base”. According to an embodiment ofthe invention, it is assumed that the characteristics of the certifiedcase base closely match those of incoming insurance applicationsreceived in a reasonable time window i.e., they form a “representativesample.” The Case Base may form the basis of all MARS model development.

At step 4215, pre-processing occurs. According to an embodiment of theinvention, one of the first steps in the model development process is tostudy the data and its various characteristics. This process may ensurethat adequate attention is given to the understanding of the problemspace. Later, appropriate pre-processing steps may be taken to extractthe maximum information out of the available data via a choice of a setof explanatory variables that have the maximum discriminatory power.According to an embodiment of the invention, as illustrated in FIG. 43,one of the early findings was the fact that for most of the candidatevariables that were chosen on the basis of experience and judgment ofthe human underwriting experts the decision boundary regions asindicated by the human experts start at the tail-end of the variabledistribution.

As described above, the decision problem may be to classify eachapplicant into risk classes, which are typically increasing in risk.Thus, as an example, the attribute denoted by the level of cholesterolin the blood of an individual may be considered. It is a known fact thata cholesterol level below 220 can be treated as almost normal. Thissuggests that in cases where the cholesterol level is at a certainlevel, such as up to about 240 at demarcation 4302, the human expertdoes not perceive a significant risk due to this factor. Thus, all caseswith a cholesterol reading below this threshold can be grouped into asingle class, e.g., “Class 1,” 4304 and the members in this class wouldnot consequently impact the response variable (e.g., the rate classdecision). As shown, a cholesterol level value of 240 is close to the75^(th) quantile 4306 of the distribution, while the value of 270 is inthe 90^(th) quantile range 4308.

One of the sub-steps may include location transformation and truncation4216. A location transformation may be considered for all variables thatexhibit the above property. Each variable may be transformed bysubtracting out its normal value. This is realized by combining theknowledge of human experts as well, since for the majority of theattributes that are health related, there are well-documented andpublished normal thresholds.

According to an embodiment of the invention, it may not be desirable todifferentiate among points within the normal ranges. Further, to focusthe classifier on those in the abnormal range, the values of thevariable may be saturated after a location transformation. In this case,the positive values may be considered, e.g.:New Value=Max(0, Old Value-Reference Value)

The above is not a limitation of the general pre-processing step aswould be applicable in other problems, but is a step relevant to theproblem domain. There were variables which had the decision boundariesdistributed fairly evenly over the entire range and did not warrant thisspecific transformation.

Further, another sub-step may include range normalization 4217. If it isdesirable to compute distances in a multi-dimensional space, e.g., tofind the closest points to a given one, it may be necessary to normalizeeach dimension. Range normalization is typically the most common way toachieve this, e.g.:

${{NewValue}\mspace{14mu}\%} = \frac{{NewValue} - {\min_{i}\left( {NewValue}_{i} \right)}}{{\max_{i}\left( {NewValue}_{i} \right)} - {\min_{i}\left( {NewValue}_{i} \right)}}$

Another sub-step may involve “tag”-encoding 4218. According to anembodiment of the invention, a specialized set of variable encoding mayalso be used to extract the maximum information out of the decisionspace. This encoding may be referred to as the “tag.” The tag isessentially an ordinal categorical variable developed from a collectionof indicators for the various decision boundaries as defined by humanexperts. These indicators are evaluated for each relevant variable inthe collection. The maximum of the individual indicators over thecollection of variables results in the final “tag.” For example, assumethat there are four key variables (out of a larger number of fields inthe case) that are highlighted by actuarial studies to determinemortality risk. Since the same studies indicate the critical thresholdsthat impact such risk, there is no reason to re-learn those thresholds.Therefore, they may be encoded in the indicator “tag.” Table 5 belowillustrates four variables: Nicotine History (NH), Body Mass Index(BMI), Cholesterol Ratio (Chol. Rat.), and Cholesterol Level (Chol.Lev.), and four groups of rules, one for each variable. According tothis example, the value of the tag starts with a default of 1 and ismodified by each applicable rule set. A running maximum of the tag valueis returned at the end, as the final result of tag.

TABLE 5 TAG 1 A) Initialize: 1 B) Fire following rules Rule # IF  1 NH < t1 2  2 <  t2 3  3 <  t3 4  4 BMI >  t4 2  5 >  t5 3  6 >  t6 4  7 > t7 5  8 >  t8 6  9 Chol. Rat. >  t9 2 10 > t10 3 11 > t11 4 12 > t12 513 > t13 6 14 Chol. Lev. > t14 2 15 > t15 3 16 > t16 4 17 > t17 5 18 >t18 6 C) Tag is determined by the MAX of the values determined by eachof the four rule sets

Thus, a tag may provide a utilization of the available human expertknowledge to obtain a boost in accuracy. By way of example, the modelswere built with and without the inclusion of the specialized “tag”variable and found that inclusion of the tag results in an improvementin accuracy by about 1-2% on average.

At step 4220, five-fold partitioning and re-sampling occurs, while adevelopment and validation set is generated at step 4225. According toan embodiment of the invention, a stratified sampling methodology may beused to partition the data set into five equal parts. The stratificationwas done along the various rate classes to ensure a consistentrepresentation in each partitioned sample. Further, a simple re-samplingtechnique may be used based on reusing each partition by taking out onepart (done five times without replacement) as a holdout and recombiningthe remaining four and using it as a development sample to build acomplete set of MARS models. This may be done five times, as mentionedearlier. By way of example, such a re-sampling and recombination wasperformed and the results were compared for consistency in accuracy, andalso to note any fundamental shift in models. The accuracy measures werefound to be closely grouped in the 94.5%-95.5% neighborhood and withmodel consistency throughout.

At step 4230, model-building experiments are performed. According to anembodiment of the invention, a variety of exploratory regression modelsmay be built and trained on the CB development sets. Further, theirclassification accuracy may be tested and validated on the CB validationsets. According to an embodiment of the invention, a parallel-network ofMARS models may evolve and develop from a global regression model and aclassification and regression trees (“CART”) model, and allows the useof MARS in the framework of a multi-class classification problem. Theglobal regression model and the classification and regression trees(“CART”) model will now be described in greater detail below.

Since this is a multi-class classification problem, by definition theresponse variable is a polychotomous categorical variable, i.e., avariable that can take values from a set of labels (e.g., “PreferredBest,” “Preferred,” “Select,” “Standard Plus,” “Standard”). However,since in this case the response is ordinal (the order of the categoricalvalues reflects the corresponding increasing risk), a risk metric may beobtained such as from an actuarial department of the insurance company.This allows the mapping of the categorical values to numerical values(e.g., reflecting mortality risk) and treating the response variable asa continuous one in order to fit a global multivariate linearregression. Using this method, a moderate fit to the data is obtained.However, the maximum accuracy achieved was about 60%, far from thedesired accuracy level of above 90%.

Additionally, a CART based model may be built using the data. Tomaintain robustness and to avoid the possibility of overfitting themodel, it may be necessary to minimize the structural complexity of theCART model. This approach yielded a CART tree with about 30 terminalnodes. Its corresponding accuracy level was substantially better thanthe global regression and was about 85%. Increasing the accuracy for thetraining sets would have resulted in deeper, more complex trees, withlarger number of terminal nodes. Such trees would exhibit overfittingtendencies and poor generalization capabilities, leading to low accuracyand robustness when evaluated on the validation sets.

From these experiments, it can be determined that a global regressionmodel, which is essentially a main-effects fit, has moderate explanatorypower, but a CART tree, which is a local non-parametric model, has amuch better performance. Since CART is essentially a pureinteraction-based model the motivation for a MARS based modeling schemawas obvious, as MARS allows both main and interaction effects to beincorporated into the model, and being a piecewise-linear adaptiveregression procedure, MARS can approximate very well any non-linearstructure (if present). Since the original motivation of development ofthe MARS algorithm stemmed from the problem of discontinuity of CARTterminal node estimates, the same benefits may apply here.

At step 4235, a parallel-network of MARS models is implemented.According to an embodiment of the invention, one issue involved thedifficulty of global models to incorporate the jumps in decisionboundaries of majority of the variables in an extremely small boundedrange. In other words, since the decision boundaries begin only afterthe 75^(th) quantile value of the explanatory variable, the shift overall other decision variables usually occur by the 95^(th) quantile. Thisissue may be addressed in a number of ways. According to one approach,“tag” encoding as explained above helps the MARS search algorithm tofind the “knots” in the right place.

According to another approach, a “parallel network” arrangement ofmodels may be used. A parallel network arrangement is a collection ofMARS models, each of which solves a binary, or two-class problem. Thismay take advantage of the fact that the response variable is ordinale.g., the decision classes being risk categories are increasing in risk.The approaches to these issues should not be considered as limitationsof the methodology presented here, but rather a property explored inorder to achieve better results. In addition, the above case generalizesto handle problems where the response may not be ordinal.

An advantage of the order of the response variable may be taken bybuilding two models each for every rate class, except the boundaryclasses, with one model for each side. For easier reference, the twomodels may be referred to as the left model and the right model. FIG. 44illustrates an example of such models. A population 4402 is divided intonon-smoking applications 4404, non-underwritten applications 4406 andnicotine applications 4408. The “Preferred” class has been broken downinto a “Preferred Left” model 4410 and “Preferred Right” model 4412. Theminimum of the two models is selected, e.g., M(Pref)=min (L,R), 2814.The results are then input into the aggregation module 4416, whichaggregates all results from the binary classifiers and selects the rateclass that best fits a given application. For example, for the rateclass “Preferred,” two models are built which estimate class membershipvalue. The “Left” model distinguishes all preferred cases from cases ofclasses, which are to the left of preferred while the “Right” model doesthe opposite. The final class membership value may be the minimum ofthese two membership values obtained. Further, in the general case wherethere is no known order amongst classes, the Left/Right models maycollapse into a single model providing with one estimated membershipvalue.

According to an embodiment of the invention, the MARS methodology may beadapted to handle logistic regression problems in the classical sense.Such an adaptation would need an adjustment of the lack-of-fit (“LOF”)criteria to be changed from least squares to logistic. However, logisticregression procedure is in itself a likelihood maximization problem thatis typically solved by using an iteratively re-weighted least squares(“IRLS”) algorithm or its counterparts. The viability of MARS may dependon the fast update criteria of the least squares LOF function, which anIRLS logistic estimation would generally prohibit.

According to an embodiment of the invention, an approximation may bemade to use the final set of MARS variables back into a SAS logisticroutine and refit. As said before, this is an approximation because ifone could ideally use logistic LOF function, then one could have derivedthe optimal set of logistic candidate variable transforms. However, are-fit process may still achieve the same degree of fit and providemodel parsimony in some of the subset models built. Also, since thelogistic function is a (0,1) map, this gives class membership valuesthat can be treated as probabilities.

According to an embodiment of the invention, a MARS module may beimplemented with software code in SAS and using MARS, where the code hasbeen trained and tested using the five-fold partitions method describedabove. By way of example of the results of such an implementation, FIG.45 illustrates a comparison matrix 4500 (with a dimensionality of k×k),whose k columns contain the set of possible decisions available to theclassifier, and whose k rows contain the correct corresponding standardreference decision, can describe a classifier's performance on a givendata set, is illustrated in FIG. 45.

In this example, agreement between the classifier and the standardreference decision occurs when the case results on the main diagonal ofmatrix 4500 while any other cell above or below the main diagonalcontains misclassified cases. In the illustrative example depicted inFIG. 45, for the second row of 4502, labeled “Preferred,” 360 out oftotal of 374 cases were correctly assigned to that rate class, while 1was assigned to “P Best,” 11 to “Select”, 1 to “Standard” and 1 to “Sendto Underwriter.”

As shown in FIG. 46, 4602 refers to the total number of agreementsbetween the classifier and the standard reference decisions fornon-smokers, while 4608 refers to the total number of agreements betweenthe classifier and the standard reference decisions for smokers. Thenotations 4604 and 4606 refer to the total number of disagreementsbetween the classifier and the standard reference decisions fornon-smokers, while 4610 and 4612 refer to the total number ofdisagreements between the classifier and the standard referencedecisions for smokers. 4614 refers to the total number of agreements notto make a decision and send the case to UW (e.g., underwriter) andnotations 4616 and 4618 refer to the total number of disagreements notto make a decision and send to UW.

Further, the matrix depicted in FIG. 46 may be used to illustrate theperformance measures used in the evaluation of the classifiers. Let N bethe total number of cases considered (in this example, N=2,920).According to the annotation in FIG. 46, N=m1 +m2+m3+m4+m5+m6+m7+m8+m9.In this example, N2=182, which is the sum of all cases that should havebeen sent to the human underwriter (i.e., m9+m7 in FIG. 46), andtherefore N1=(2,920−182)=2,738. Three measures of performance for theclassifier may be used, where M(ij) is a cell in the matrix shown inFIG. 45:

-   -   Coverage: the total number of decisions made by the classifier        as a percentage of the total number of cases considered, i.e.:

${Coverage} = {\sum\limits_{i = 1}^{k}{\sum\limits_{j = 1}^{k - 1}{{M\left( {i,j} \right)}/N}}}$

Using the annotations defined in FIG. 46, coverage may be redefined as:

${Coverage} = \frac{\left( {{m\; 1} + {m\; 2} + {m\; 3}} \right) + \left( {{m\; 4} + {m\; 5} + {m\; 6}} \right) + {m\; 9}}{\left( {{m\; 1} + {m\; 2} + {m\; 3}} \right) + \left( {{m\; 4} + {m\; 5} + {m\; 6}} \right) + \left( {{m\; 7} + {m\; 8} + {m\; 9}} \right)}$

Thus, in the example depicted in FIG. 45 the coverage is:(2,920−242)/2,920=91.71%. An addition performance measure may include:

-   -   Relative Accuracy: the total number of correct decisions made by        the classifier as a percentage of the total number of decisions        made, i.e.:

${{Relative}\mspace{14mu}{Accuracy}} = {\sum\limits_{i = 1}^{k - 1}{{M\left( {i,i} \right)}/{\sum\limits_{i = 1}^{k}{\sum\limits_{j = 1}^{k - 1}{M\left( {i,j} \right)}}}}}$

Using the annotations defined in FIG. 46, the relative accuracy may beredefined as:

${RelativeAccuracy} = \frac{{m\; 1} + {m\; 4}}{\left( {{m\; 1} + {m\; 2} + {m\; 3}} \right) + \left( {{m\; 4} + {m\; 5} + {m\; 6}} \right) + {m\; 9}}$

In the example depicted in FIG. 45 the relative accuracy is:(2,558)/(2,920−242) =95.52%. An further performance measure may include:

-   -   Global Accuracy: the total number of correct decisions made by        the classifier as a percentage of the total number of cases        considered, i.e.:

${{Global}\mspace{14mu}{Accuracy}} = {\sum\limits_{i = 1}^{k}{{M\left( {i,i} \right)}/{\sum\limits_{i = 1}^{k}{\sum\limits_{j = 1}^{k}{M\left( {i,j} \right)}}}}}$

Again, using the annotations defined in FIG. 46, the global accuracy maybe redefined as:

${GlobalAccuracy} = \frac{{m\; 1} + {m\; 4} + {m\; 7}}{\left( {{m\; 1} + {m\; 2} + {m\; 3}} \right) + \left( {{m\; 4} + {m\; 5} + {m\; 6}} \right) + \left( {{m\; 7} + {m\; 8} + {m\; 9}} \right)}$

In the example depicted in FIG. 45 the global accuracy is:2,734/2,920=93.63%. Coverage and relative accuracy may be competingobjectives. By establishing a confidence metric for the classifieroutput, one could adjust a confidence threshold to achieve varioustradeoffs between accuracy and coverage. At one extreme, one could havea very low tradeoff, accepting any output (this would yield 100%coverage but very low accuracy). At the other extreme, one could havevery high confidence thresholds. This would drastically reduce coveragebut increase relative accuracy.

The results of networks of MARS (or Neural Networks, as described below)models could also be post-processed to establish an alternativeconfidence metric that could be used to achieve other tradeoffs betweenaccuracy and coverage. The tables set forth in FIG. 47 describe theperformance of the network of MARS models on each of the fivepartitions. For each partition, the global and relative accuracy islisted, with the corresponding coverage. The results are shown with andwithout post-processing.

Each of these partitions (e.g., Partition 1, 4710, Partition 2, 4720,Partition 3, 4730, Partition 4, 4740 and Partition 5, 4750) shows theperformance results of the network of MARS models applied to 80% of thedata used to build the model (training set 4760) and 20% of the datathat was withheld from the model construction (validation set 4770). Thetables in FIG. 48 summarize the minimum 4810, maximum 4820, and average4830 results of applying the network of MARS models to the fivepartitions.

These tables illustrate that the average performance of a network ofMARS models, applied to the five partitions, was very accurate. Inparticular a relative accuracy of 95% on the validation set 4840 of FIG.48, with coverage of about 90% may be extremely good and useful forquality assurance. An analysis of the minimum and maximum achieved mayalso show a high level of robustness, exemplified by the relativelytight range of performance values.

The technical considerations that go into a MARS model are well knownand can be found in Friedman's original paper in the Annals ofStatistics, the contents of which are incorporated herein by reference.However, to better illustrate the present invention, it is useful todescribe a few basic points adopted in the MARS tuning as well as someadditional steps that may be necessary to ensure a robust model buildingprocess.

General MARS parameters may include overfit and cost-complexity pruning,cross-validation, and multi-collinearity. According to an embodiment ofthe invention, MARS is essentially a recursive-partitioning procedure.The partitioning is done at points of the various explanatory variablesdefined as “knots” and overall optimization is achieved by performingknot optimization over the lack-of-fit criteria. Moreover, to achievecontinuity across partitions MARS employs a two-sided power basisfunction of the form:

b_(q)^(±)(x − t) = [±(x − t)]₊^(q).

However, in this case, a linear-piecewise basis q=1 is used. Here ‘t’ isthe knot around which the basis is formed. It may be important to use anoptimal number of basis functions to guard against possible overfit. Byway of example, an experiment may be performed with one dataset bystarting from a small number of maximal basis functions and building itup to a medium size number and use the cost-complexity notion developedin CART methodology and deployed in MARS to prune back and find abalance in terms of optimality which provides an adequate fit. In thisexample, the use of cost-complexity pruning revealed that 25-30 basisfunctions were sufficient.

Another important criteria which affects the pruning is the estimateddegrees of freedom allowed. This may be done by using ten-fold crossvalidation from the data set for each model.

In addition, there is no explicit way by which MARS can handlemulti-collinearity. However, MARS does provide a parameter thatpenalizes the separate choice of correlated variables in a downstreampartition. MARS then works with the original parent instead of choosingother alternates. According to an embodiment of the invention, a mediumpenalty may be used to take care of this problem.

Further, optimization of cut-offs using evolutionary algorithms (“EA”)may be used. When a new case comes in, it is evaluated by the completeset of models and a class membership distribution is obtained for everyincoming case. Next in line comes the problem of assigning rate-classesto the incoming case. One alternative may be to use hand-tuned cut-offscomputed through simple tools like Microsoft Excel based solver. Theseresults may be compared to an EA based optimized cut-off set. By way ofexample, an evolutionary algorithm may provide a boost in accuracy byabout 1% as compared to the hand-tuned cut-offs.

5. Neural Network Classifier

Another aspect of the present invention may provide a method and systemto implement a neural network classifier with multiple classes forautomated insurance underwriting and its quality assurance. Neuralnetworks may be advantageous, as they can approximate any complexnonlinear function with arbitrary accuracy (e.g., they are universalfunctional approximators). Neural networks are generally non-parametricand data-driven. That is, they approximate the underlying nonlinearrelationship through learning from examples with few a prioriassumptions about the model. In addition, neural networks are able toprovide estimates of posterior probabilities. Such posterior probabilityvalues may be useful for obtaining the highest possible decisionaccuracy in the classifier fusion or other decision-making processes.

There are a variety of types of neural networks. However, neuralnetworks can be broadly categorized into two main classes, i.e.,feed-forward and recurrent (also called feed back) neural networks.Among all these types, multiple-layer feed-forward neural networks areoften used for classification. Neural networks can be directly appliedto solve both dichotomous and polychotomous classification problems.However, it is generally more accurate and efficient when neuralnetworks are used for two-class (e.g., dichotomous) classificationproblems. As the number of classes increases, direct use of multi-classneural networks may encounter difficulties in training and in achievingthe desired performance.

As previously described, insurance underwriting problems may ofteninvolve the use of large numbers of features in the decision-makingprocess. The features typically include the physical conditions, medicalinformation, and family history of the applicant. Further, insuranceunderwriting frequently has a large number of risk categories (e.g.,rate classes). The risk category of an application is traditionallydetermined by using a number of rules/standards, which often have theform of “if the value of feature x exceeds a, then the application can'tbe rate class C, i.e., has to be lower than C”. These types of decisionrules, 4930 and 4940 in FIG. 49, “clip” the decision surface. Decisionrules interpreted and used by a human underwriter may form an overallpiecewise-continuous decision boundary, as shown in the graph of FIG.49.

To design a neural network classifier to achieve a comparableperformance (e.g., accuracy and coverage) as rule-based classifiers forinsurance underwriting, various issues may need to be addressed. First,a neural network may need to deal with a large number of features andtarget classes. The large number of features and high number of targetclasses call for a high degree of complexity of neural network (“NN”)structure (e.g., more nodes and more parameters to learn, i.e. higherDegrees of Freedom (DOF). Such complex NN structures may require moretraining data for properly training the network and achieving reasonablegenerality (performance). However, sufficient data may be difficult toobtain. Even with sufficient data, the complex neural network structurerequires enormous training time and computational resources. Moreimportantly, complex NN structures (high DOF) tend to have more localminima, and thus, training is prone to fall into local minima and failsto achieve global minimization. As a result, it usually difficult toachieve a desired performance for a neural network with complexstructure.

Another issue to be addressed involves incorporating domain knowledgeinto the neural network classification process. As discussed before, thediscrete rules that human underwriters use for risk category assignmentform an overall piecewise-continuous decision boundary in the featurespace and neural networks may have difficulty learning the decisionboundary due to the insufficient data points being available. One way toalleviate the difficulty and improve the performance of the neuralnetwork may be to directly incorporate the rules into the neural networkmodel and use these rules as additional information to “guide” networklearning.

One aspect of the present invention is related to a method and system ofimproving the performance of neural network classifiers, so that theneural network classifier can perform automated insurance underwritingand its quality assurance with a level of accuracy and reliability thatis comparable to the rule-based production decision engine.Specifically, this invention improves the performance of classifiers bydecomposing a multi-class classification problem into a series of binaryclassification problems. Each of the binary classifiers may classify oneindividual class from the other classes and the final class assignmentfor an unknown input will be decided based on the outputs of all of theindividual binary classifiers.

Additionally, as another way to improve the classifier performance, thisinvention incorporates the domain knowledge of the human underwriterinto a neural network design. The domain knowledge, represented by anumber of rules, may be integrated into a classifier by using anauxiliary feature, the value of which is determined by the rules.Moreover, to further improve the classifier performance, this inventionmay also analyze the outputs of the individual binary classifiers toidentify the difficult cases for which the classifier cannot make asolid decision. To reduce misclassification rate, these difficult casesmay then be sent to a human underwriter for further analysis.

In the conventional design of multi-class neural network classifiers asingle neural network contains multiple output nodes. According to anembodiment of the invention, decomposing the multi-class classifier intomultiple binary classifiers may solve a multi-class classificationproblem. For the purposes of illustration, assume that a hypotheticallife insurance company has risk categories “Cat1”, “Cat2”, “Cat3”,“Cat4”, and “Cat5”. A rating of “Cat1” is the best risk, while “Cat5” isthe worst. Then, the concept of the multi-class classifier decompositionused in this invention can be illustrated in the example of FIG. 50.Each binary classifier (5010, 5020, 5030) is for one class and istrained to classify the specific class (the “class”) and the rest of theclasses combined (the “others”). Before training each of the binaryclassifiers, the training set is relabeled “1” for the data points inthe “class” group and “0” for the data points in the “others” group.When performing classification for a new input case, each of the binaryclassifiers determines the probability that the new case belongs to theclass for which the binary classifier is responsible. Therefore, theoutput of the neural network is a number in the [0,1] interval. Thefinal class for the new input case is assigned by the MAX decision rule5040. For example, an application may receive a “0.6 and a 1” in theCat3 and Cat4 categories, respectively, and a “0” in the Cat1, Cat2, andCat5 risk categories. The MAX decision rule 5040 may then select theCat4 risk category.

According to an embodiment of the invention, for each of the binaryclassifiers designed in the current invention, the neural network ismultiple-layer feed-forward in type and has one hidden layer. However,for other applications, using different neural network types with morethan one hidden layer may be explored for obtaining better performance.It is therefore to be understood that the current invention is notlimited to one hidden layer feed-forward neural networks. Instead, themethod may work equally well for multiple numbers of hidden layers.

According to an embodiment of the invention, domain knowledge may beintegrated into neural network learning by representing the knowledgewith an auxiliary feature. The domain knowledge may be first representedby a series of rules. A typical rule has the following format (onceagain using the afore-mentioned five hypothetical rate classes): “If theapplicant's cholesterol level exceeds 252, he does not qualify for rateclass C1, i.e., the best rate class for him is C2”. Formally, this rulecan be expressed in a general IF-THEN rule as follows.IF x_(i)>t_(i,j), THEN the best available rate class is C_(j)

where x_(i) is the i^(th) feature, t_(i,j) is the j^(th) threshold ofthe i^(th) feature, and C_(j) is the j^(th) rate class. Theincorporation of domain knowledge is further described below.

According to an embodiment of the invention, the classifier designprocess for a neural network classifier may comprise data preprocessing,classifier design and optimization, and post-processing. These threeaspects are described in greater detail below.

Data preprocessing may include range normalization and featureextraction and selection. According to an embodiment of the invention,range normalization is a process of mapping data from the original rangeto a new range. Normalization may be generally problem specific.However, it is often done either for convenience or for satisfying theinput requirements of the algorithm(s) under consideration. For patternclassification problems, one purpose of normalization is to scale allfeatures the classifier is using to a common range so that effects dueto arbitrary feature representation (e.g., different units) can beeliminated. In addition, some classifiers, such as neural networks,require a range of input to be normalized.

One way to normalize data is range normalization. To normalize the databy range, the feature value is divided by its range, i.e., thedifference between the maximum and the minimum of the feature value. Letx_(i,j) be value of the i^(th) data point of the j^(th) feature.

Then the normalized value y_(i,j) is:

$\begin{matrix}{y_{i,j} = \frac{x_{i,j} - {\min\left( x_{,j} \right)}}{{\max\left( x_{,j} \right)} - {\min\left( x_{,j} \right)}}} & (1)\end{matrix}$

The normalized values y_(i,j) will be in the range of [0, 1]. The rangenormalization requires knowing the minimum and the maximum values of thedata. The greatest advantage of this normalization is that it introducesno distortion to the variable distribution, as the instance values andtheir corresponding normalized values have a linear relationship. Thatis, given two instance values with the first being twice the second,when they are normalized the first normalized value will still be twicethe second normalized value. This is why range normalization is alsocalled linear scaling or linear transformation.

Another type of data preprocessing may involve featureextraction/selection. For example, raw data is placed within a 20-columnspreadsheet. The first column is the applicant ID number and the secondcolumn is the rate class. Columns 3 through 20 are theattributes/variables/features for the applicant. Instead of directlyusing the 18 original features, two new features are derived. The firstderived feature is the body mass index (“BMI”). Underwriter experiencehas shown that the BMI has more discriminating power in classification.The second derived feature, tag, is used to represent the domainknowledge in neural network training. The two derived features arefurther described below.

As described above, BMI is defined as ratio of weight in kilogram andthe height squared in meters. Let wt be the weight in pounds and Ht bethe height in inches. BMI can be expressed as:

$\begin{matrix}{{BMI} = \frac{{wt} \times 2.2046}{\left\lbrack {{Ht} \times 0.0254} \right\rbrack^{2}}} & (2)\end{matrix}$

One approach for incorporating domain knowledge into the neural networkmodeling involves training by hints, as described by Abu-Mostafa (1993),where almost any type of prior knowledge can be incorporated into aneural network through constructing the hints. Although the technique isflexible, it may be of a limited application in neural networks.According to an embodiment of the invention, domain knowledge isincorporated into the neural network classifier by using an artificialfeature, such as tag. The tag feature may take different values based ona set of rules that represent the domain knowledge.

By way of example, the five family history features, such as fromcolumns 3-7, are condensed and represented by two features, FH1 and FH2.While the FH1 feature has the binary values of 0 or 1, FH2 has thetriple values of 0,1, and 2. The values of FH1 and FH2 are determined bythe following rules, where the terms age_sib_card_canc_diag,age_moth_card_canc_diag, age_fath_card_canc_diag, age_moth_card_death,age_fath_card_death respectively correspond to the age when a sibling ofthe applicant was diagnosed with a cardiac or cancer disease, the agewhen the mother of the applicant was diagnosed with a cardiac or cancerdisease, the age when the father of the applicant was diagnosed with acardiac or cancer disease, the age when the mother of the applicant dieddue to a cardiac disease, and the age when the father of the applicantdied due to a cardiac disease. For a given applicant, one or more ofthese terms may be not applicable.

IF (age_sib_card_canc_diag ≦ 60)   OR (age_moth_card_canc_diag ≦ 60)  OR (age_fath_card_canc_diag ≦ 60), THEN FH₁ is 1. Otherwise, FH₁ is 0.IF (age_moth_card_death ≦ 60) OR (age_fath_card_death ≦ 60), THEN FH₂=1.IF (age_moth_card_death ≦ 60) AND (age_fath_card_death ≦ 60), THENFH₂=2. Otherwise, FH₂=0.

Examples of rules that may be used to compute TAG are listed below inTable 6.

TABLE 6

As indicated earlier, domain knowledge may be represented by a set ofrules. A typical rule may have the following format (once again usingthe afore-mentioned five hypothetical rate classes): “If the applicant'scholesterol level exceeds 252, he does not qualify for rate class C1,i.e., the best rate class for him is C2”. For example, this rule can beexpressed in a general IF-THEN rule as follows:IF x_(i)>t_(i,j), THEN the best available rate class is C_(j)

Where x_(i) is the i^(th) feature, t_(i,j) is the j^(th) threshold ofthe i^(th) feature, and C_(j) is the j^(th) rate class

A vector with binary number “0” or “1” may be used to represent theconsequent part of the IF-THEN rule. For example, [0, 1, 1, 1, 1] meansthe best rate class of C₂ while [0, 0, 0, 1, 1] means the best rateclass of C₄.

For each data point in the training data set, all rules that “fire” arechecked and the intersection (e.g., the Boolean logic minimum) of thevector of the firing rule is calculated, as well as the vector that hasinitial value of all ones. The value of the auxiliary feature may thenbe determined by counting the number of ones in the final vector. As canbe seen, the auxiliary feature takes integer numbers ranging from one to

FOR each of the data points in the training set   Initialize vectorV=[1, 1, 1, 1, 1]   FOR each of the rules     IF the i^(th) rule isfired, THEN V=V & Vi (“&” is logic AND)   END of all rules   The valueof the auxiliary feature = the number of ones in the vector V. END ofall data pointsthe number of rate classes. The pseudo-code shown summarizes theprocedure of determining the value of the auxiliary feature.

After obtaining the value of the auxiliary feature for each data point,the auxiliary feature may be treated as a regular feature and includedinto the final feature set. The neural network may then be trained andtested with the final feature set. Because of the additional informationprovided by the auxiliary feature, the neural network may be “guided”during learning to more quickly find the piecewise continuous decisionboundary, which not only reduces the training time and efforts, but mayalso improve the classification performance of neural networkclassifier.

Additional features that may be used for neural network classifierdesign include, but are not limited to, tag, BMI, diastolic and/orsystolic blood pressure readings, cholesterol level, cholesterol ratio,various liver enzymes, such as SGOT (Serum Glutamic OxaloaceticTransaminase), SGPT (Serum Glutamic Pyruvic Transaminase), GGT (GalactanGalactosyl Transferase), nicotine use history, and various aspects offamily history.

There are a number of types of neural networks. According to anembodiment of the invention, a three-layer feed-forward neural networkwith back propagation learning may be used. Two separate models may beused for nicotine and non-nicotine cases, respectively. By way ofexample, for nicotine cases, there may be three rate classes, e.g.,“Preferred_nic,” “Standardplus_nic,” and Standard_nic, whilenon-nicotine cases may have five rate classes, e.g., “Best,”“Preferred,” “Select,” “Standardplus,” and “Standard.” Both models aremultiple-class classifiers. A neural network with multiple output nodesmay be a typical design for multiple-class classifiers where each of theneutral network output nodes corresponds to each class. However, neuralnetworks with multiple output nodes may have a large number of weightsand biases, and thus require a large training data set and more trainingtime for properly training the network. If the data size is relativelysmall compared to the number of features and the number of classes,multiple binary neural networks may be used to perform themultiple-class classification. Using multiple binary-networks may reducethe complexity of the network, thus reducing the training time, but alsomay improve the classification performance. An example of thearchitecture of a neural network classifier is illustrated in FIG. 51.The non-nicotine model 5110 has five binary classifiers 5120 while thenicotine model 5130 has three binary classifiers 5140. Each model 5110,5130 has a MAX function 5150 and 5160. Applications in the non-nicotinemodel 5110 are then assigned to the appropriate rate class 5170, whileapplications in the nicotine model 5130 are assigned to the appropriaterate class 5180.

In the example of FIG. 51, each binary network has the structure of12-5-1, e.g., twelve input nodes, five hidden neurons, and one outputnode. Activation functions for both hidden and output neurons may belogistic sigmoidal functions. According to an embodiment of theinvention, the range of target values may scaled to [0.1 0.9] to preventsaturation during training process. The Levenberg-Marquardt numericaloptimization technique may be used as the backpropagation-learningalgorithm to achieve second-order training speed.

Each binary network represents an individual rate class and is trainedwith the targets of one-vs-other. During classification for an unknowncase, each network provides the probability of the unknown casebelonging to the class it represents. The final rate class of theunknown case is determined by the MAX decision rule, e.g., given avector whose entry values are in the interval [0,1], the MAX rule willreturn the value of the position of the largest entry.

To further improve the classification performance, it may beadvantageous to apply some post-processing techniques to the outputs ofthe individual networks, prior to the MAX decision making process.Instead of assigning rate class to an unknown case just based on themaximum outputs of the individual networks, the distribution of theoutputs is characterized. If the distribution of the outputs does notmeet certain pre-defined criteria, no decision needs to be made by theclassifier. Rather, the case will be sent to human underwriter forevaluation. The rationale here is that if a correct decision cannot bemade, it would be preferable that the classifier makes no decisionrather than the wrong decision. Considering the neutral network outputsas discrete membership grade for all rate classes, the four featuresthat characterize the membership grades may be the same as those setforth above with respect to the fusion module discussed above, i.e.,cardinality, entropy, the difference between the highest and the secondhigh values of outputs, and the separation between rank orders of thehighest and the second highest values of outputs.

Again, with the features defined for characterizing the network outputs,the following two-step criteria may be used for “rejecting” the cases:C<τ₁ OR C>τ₂ OR E>τ₃  Step 1D<τ₄ AND S≦1  Step 2

Where τ₁, τ₂, τ₃, and τ₄ are the thresholds. The value of the thresholdsis typically data set dependent. In this embodiment, the value of thethresholds are first empirically estimated and then fine-tuned byevolutionary algorithms (EA). The final numbers for all five-fold datasets are illustrated in Table 7 below:

TABLE 7 Run#1 Run#2 Run#3 Run#4 Run#5 Non-nicotine model τ₁ 0.5 0.5 0.50.5 0.5 τ₂ 2.0 2.0 2.0 2.0 2.0 τ₃ 0.9 0.9 0.9 0.93 0.98 τ₄ 0.1 0.15 0.10.1 0.07 Nicotine model τ₁ 0.3 0.3 0.3 0.3 0.3 τ₂ 1.75 1.75 1.75 1.751.75 τ₃ 0.85 0.85 0.8 0.85 0.85 τ₄ 0.2 0.25 0.2 0.2 0.2

According to an embodiment of the invention, a neural network classifiermay be implemented using software code, and tested against a case base.By way of example, a software implementation of a neural network may usea case base of 2,879 cases. After removal of 173 UW cases, the remaining2,706 cases were used for training and testing the neural networkclassifier. Five-fold cross-validation was used to estimate theperformance of the classifier.

The combined confusion matrices of the five-fold runs are illustrated inFIG. 52. For comparison, the combined confusion matrices for thefive-fold runs after post-processing are illustrated in FIG. 53. Theperformance for this example before post-processing is provided in FIG.54, while the performance for this example after post-processing isprovided in FIG. 55.

According to an embodiment of the invention, the systems and processesdescribed in this invention may be implemented on any general purposecomputational device, either as a standalone application orapplications, or even across several general purpose computationaldevices connected over a network and as a group operating in aclient-server mode. According to another embodiment of the invention, acomputer-usable and writeable medium having a plurality of computerreadable program code stored therein may be provided for practicing theprocess of the present invention. The process and system of the presentinvention may be implemented within a variety of operating systems, suchas a Windows® operating system, various versions of a Unix-basedoperating system (e.g., a Hewlett Packard, a Red Hat, or a Linux versionof a Unix-based operating system), or various versions of anAS/400-based operating system. For example, the computer-usable andwriteable medium may be comprised of a CD ROM, a floppy disk, a harddisk, or any other computer-usable medium. One or more of the componentsof the system or systems embodying the present invention may comprisecomputer readable program code in the form of functional instructionsstored in the computer-usable medium such that when the computer-usablemedium is installed on the system or systems, those components cause thesystem to perform the functions described. The computer readable programcode for the present invention may also be bundled with other computerreadable program software. Also, only some of the components may beprovided in computer-readable code.

Additionally, various entities and combinations of entities may employ acomputer to implement the components performing the above-describedfunctions. According to an embodiment of the invention, the computer maybe a standard computer comprising an input device, an output device, aprocessor device, and a data storage device. According to otherembodiments of the invention, various components may be computers indifferent departments within the same corporation or entity. Othercomputer configurations may also be used. According to anotherembodiment of the invention, various components may be separate entitiessuch as corporations or limited liability companies. Other embodiments,in compliance with applicable laws and regulations, may also be used.

According to one specific embodiment of the present invention, thesystem may comprise components of a software system. The system mayoperate on a network and may be connected to other systems sharing acommon database. Other hardware arrangements may also be provided.

Other embodiments, uses and advantages of the present invention will beapparent to those skilled in the art from consideration of thespecification and practice of the invention disclosed herein. Thespecification and examples should be considered exemplary only. Theintended scope of the invention is only limited by the claims appendedhereto.

While the invention has been particularly shown and described within theframework of an insurance underwriting application, it will beappreciated that variations and modifications can be effected by aperson of ordinary skill in the art without departing from the scope ofthe invention. For example, one of ordinary skill in the art willrecognize that certain classifiers can be applied to any othertransaction-oriented process in which underlying risk estimation isrequired to determine the price structure (e.g., premium, price,commission, etc.) of an offered product, such as insurance,re-insurance, annuities, etc. Furthermore, one of ordinary skill in theart will recognize that such decision engines do not need to berestricted to insurance underwriting applications.

1. A process for underwriting an insurance application based on aplurality of previous insurance applications and their associatedunderwriting decisions, the process implemented using a computerreadable medium and the process performed by a computer, the processcomprising: creating, by the computer, a plurality of binary classifiersbased on a structured methodology of multivariate adaptive regressionsplines (“MARS”), where the plurality of binary classifiers are arrangedin a parallel network; identifying, by the computer, a relevant set ofMARS variables and parameters based on the plurality of previousinsurance applications and their associated underwriting decisions;performing, by the computer, at least one cross-validation technique andat least one re-sampling technique on the plurality of previousinsurance applications and their associated underwriting decisions;modifying, by the computer, the plurality of binary classifiers based onthe performance of the at least one cross-validation technique and theat least one re-sampling technique; outputting, by the computer, aclassification assignment for the at least one new insurance applicationutilizing a validated parallel network; and performing processing on theclassification assignment for the at least one new insurance applicationto generate an underwriting assessment of the at least one new insuranceapplication.
 2. The process according to claim 1, where the at least onecross-validation technique and the at least one re-sampling techniquefurther comprises performing a location transformation by subtracting anormal value.
 3. The process according to claim 1, where the at leastone cross-validation technique and the at least one re-samplingtechnique further comprising normalizing the range of the plurality ofapplications.
 4. The process according to claim 1, further comprisinggenerating a tag for the insurance application, wherein the tag isgenerated based on the previous insurance applications and theirassociated underwriting decisions.
 5. The process according to claim 1,where the at least one re-sampling technique further comprises:partitioning data from the previous insurance applications and theirassociated underwriting decisions into five groups of equal size;removing one of the five groups; and combining the remaining four groupsin a development sample.
 6. The process according to claim 1, furthercomprising: digitizing the insurance application and the plurality ofprevious insurance application underwriting decisions; and generating acasebase of the plurality of previous insurance application underwritingdecisions, where processing occurs based at least in part on thecontents of the casebase.
 7. The process according to claim 1, furthercomprising fusing the classification assignment for the at least oneinsurance application with at least one other classification assignmentfor the insurance application, where the at least one other classifieris generated by at least one other classifier.
 8. A process forunderwriting an insurance application based on a plurality of previousinsurance applications and their associated underwriting decisions, theprocess implemented on a tangibly embodied computer readable medium andperformed by a computer, the process comprising: digitizing, by thecomputer, the insurance application and the plurality of previousinsurance application underwriting decisions; generating, by thecomputer, a casebase of the plurality of previous insurance applicationunderwriting decisions; creating, by the computer, a plurality of binaryclassifiers based on a structured methodology of multivariate adaptiveregression splines (“MARS”), where the plurality of binary classifiersare arranged in a parallel network; identifying, by the computer arelevant set of MARS variables and parameters based on the plurality ofprevious insurance applications and their associated underwritingdecisions; performing, by the computer, at least one cross-validationtechnique and at least one re-sampling technique on the plurality ofprevious insurance applications and their associated underwritingdecisions, where the at least one re-sampling technique furthercomprises: partitioning data from the previous insurance applicationsand their associated underwriting decisions into five groups of equalsize; removing one of the five groups; and combining the remaining fourgroups in a development sample; modifying, by the computer, theplurality of binary classifiers based on the performance of the at leastone cross-validation technique and the at least one re-samplingtechnique; outputting, by the computer, a classification assignment forthe at least one new insurance application utilizing a validatedparallel network; and fusing, by the computer, the classificationassignment for the at least one insurance application with at least oneother classification assignment for the insurance application, where theat least one other classifier is generated by at least one otherclassifier.
 9. The process according to claim 8, where the at least onecross-validation technique and the at least one re-sampling techniquefarther comprises performing a location transformation by subtracting anormal value.
 10. The process according to claim 8, where the at leastone cross-validation technique and the at least one re-samplingtechnique further comprising normalizing the range of the plurality ofapplications.
 11. The process according to claim 8, further comprisinggenerating a tag for the insurance application, wherein the tag isgenerated based on the previous insurance applications and theirassociated underwriting decisions.
 12. A computer readable medium havingcode for causing a processor to underwrite an insurance applicationbased on a plurality of previous insurance application underwritingdecisions, the medium comprising: code for creating a plurality ofbinary classifiers based on a structured methodology of multivariateadaptive regression splines (“MARS”), where the plurality of binaryclassifiers are arranged in a parallel network; code for identifying arelevant set of MARS variables and parameters based on the plurality ofprevious insurance applications and their associated underwritingdecisions; code for performing at least one cross-validation techniqueand at least one re-sampling technique on the plurality of previousinsurance applications and their associated underwriting decisions; codefor modifying the plurality of binary classifiers based on theperformance of the at least one cross-validation technique and the atleast one re-sampling technique; and code for outputting aclassification assignment for the at least one new insurance applicationutilizing a validated parallel network.
 13. The medium according toclaim 12, where the at least one cross-validation technique and the atleast one re-sampling technique further comprises performing a locationtransformation by subtracting a normal value.
 14. The medium accordingto claim 12, where the at least one cross-validation technique and theat least one re-sampling technique further comprising normalizing therange of the plurality of applications.
 15. The medium according toclaim 12, further comprising code for generating a tag for the insuranceapplication, wherein the tag is generated based on the previousinsurance applications and their associated underwriting decisions. 16.The medium according to claim 12, where the at least one re-samplingtechnique further comprises: partitioning data from the previousinsurance applications and their associated underwriting decisions intofive groups of equal size; removing one of the five groups; andcombining the remaining four groups in a development sample.
 17. Themedium according to claim 12, further comprising: code for digitizingthe insurance application and the plurality of previous insuranceapplication underwriting decisions; and code for generating a casebaseof the plurality of previous insurance application underwritingdecisions, where processing occurs based at least in part on thecontents of the casebase.
 18. The medium according to claim 12, furthercomprising code for fusing the classification assignment for the atleast one insurance application with at least one other classificationassignment for the insurance application, where the at least one otherclassifier is generated by at least one other classifier.
 19. A computerreadable medium having executable code stored on it, which, whenexecuted by the computer, causes a processor to underwrite an insuranceapplication based on a plurality of previous insurance applicationunderwriting decisions, the medium comprising: code for digitizing theinsurance application and the plurality of previous insuranceapplication underwriting decisions; code for generating a casebase ofthe plurality of previous insurance application underwriting decisions;code for creating a plurality of binary classifiers based on astructured methodology of multivariate adaptive regression splines(“MARS”), where the plurality of binary classifiers are arranged in aparallel network; code for identifying a relevant set of MARS variablesand parameters based on the plurality of previous insurance applicationsand their associated underwriting decisions; code for performing atleast one cross-validation technique and at least one re- samplingtechnique on the plurality of previous insurance applications and theirassociated underwriting decisions, where the at least one resamplingtechnique further comprises: partitioning data from the previousinsurance applications and their associated underwriting decisions intofive groups of equal size; removing one of the five groups; andcombining the remaining four groups in a development sample; code formodifying the plurality of binary classifiers based on the performanceof the at least one cross-validation technique and at least onere-sampling technique; code for outputting a classification assignmentfor the at least one new insurance application utilizing a validatedparallel network; and code for fusing the classification assignment forthe at least one insurance application with at least one otherclassification assignment for the insurance application, where the atleast one other classifier is generated by at least one otherclassifier.
 20. The medium according to claim 19, where the at least onecross-validation technique and the at least one re-sampling techniquefurther comprises performing a location transformation by subtracting anormal value.
 21. The medium according to claim 19, where the at leastone cross-validation technique and the at least one re-samplingtechnique further comprising normalizing the range of the plurality ofapplications.
 22. The medium according to claim 19, further comprisingcode for generating a tag for the insurance application, wherein the tagis generated based on the previous insurance applications and theirassociated underwriting decisions.
 23. A system to underwrite aninsurance application based on a plurality of previous insuranceapplication underwriting decision, the system comprising: a classifiermodule for creating a plurality of binary classifiers based on astructured methodology of multivariate adaptive regression splines(“MARS”), where the plurality of binary classifiers are arranged in aparallel network; an identification module for identifying a relevantset of MARS variables and parameters based on the plurality of previousinsurance applications and their associated underwriting decisions; aprocessor in the form of a tangibly embodied computer processor, theprocessor performing at least one cross-validation technique and atleast one re-sampling technique on the plurality of previous insuranceapplications and their associated underwriting decisions, and where theclassifier module modifies the plurality of binary classifiers based onthe performance of the at least one cross-validation technique and atleast one re-sampling technique to build a robust set of binaryclassifiers; and an output module utilizing a validated parallel networkfor outputting a classification assignment for the at least one newinsurance application.
 24. The system according to claim 23, where theat least one cross-validation technique and the at least one re-samplingtechnique further comprises performing a location transformation bysubtracting a normal value.
 25. The system according to claim 23, wherethe at least one cross-validation technique and the at least onere-sampling technique further comprising normalizing the range of theplurality of applications.
 26. The system according to claim 23, furthercomprising a tag module for generating a tag for the insuranceapplication, wherein the tag is generated based on the previousinsurance applications and their associated underwriting decisions. 27.The system according to claim 23, where the at least one re-samplingtechnique further comprises: partitioning data from the previousinsurance applications and their associated underwriting decisions intofive groups of equal size; removing one of the five groups; andcombining the remaining four groups in a development sample.
 28. Thesystem according to claim 23, further comprising: a digitizer fordigitizing the insurance application and the plurality of previousinsurance application underwriting decisions; and a storage module forcreating a casebase of the plurality of previous insurance applicationunderwriting decisions, where processing occurs based at least in parton the contents of the casebase.
 29. The system according to claim 23,further comprising a fuser for fusing the classification assignment forthe at least one insurance application with at least one otherclassification assignment for the insurance application, where the atleast one other classifier is generated by at least one otherclassifier.
 30. A system to underwrite an insurance application based ona plurality of previous insurance application underwriting decisions,the system comprising: a digitizer for digitizing the insuranceapplication and the plurality of previous insurance applicationunderwriting decisions; a storage module for creating a casebase of theplurality of previous insurance application underwriting decisions,where processing occurs based at least in part on the contents of thecasebase; a classifier module for creating a plurality of binaryclassifiers based on a structured methodology of multivariate adaptiveregression splines (“MARS”), where the plurality of binary classifiersare arranged in a parallel network; an identification module foridentifying a relevant set of MARS variables and parameters based on theplurality of previous insurance applications and their associatedunderwriting decisions; a processor in the form of a tangibly embodiedcomputer processor, the processor performing at least onecross-validation technique and at least one re-sampling technique on theplurality of previous insurance applications and their associatedunderwriting decisions, where the classifier module modifies theplurality of binary classifiers based on the performance of the at leastone cross-validation technique and at least one re-sampling technique tobuild a robust set of binary classifiers and where the at least onere-sampling technique further comprises: partitioning data from theprevious insurance applications and their associated underwritingdecisions into five groups of equal size; removing one of the fivegroups; and combining the remaining four groups in a development sample;an output module utilizing a validated parallel network for outputting aclassification assignment for the at least one new insuranceapplication; and a fuser for fusing the classification assignment forthe at least one insurance application with at least one otherclassification assignment for the insurance application, where the atleast one other classifier is generated by at least one otherclassifier.
 31. The system according to claim 30, where the at least onecross-validation technique and the at least one re-sampling techniquefurther comprises performing a location transformation by subtracting anormal value.
 32. The system according to claim 30, where the at leastone cross-validation technique and the at least one re-samplingtechnique further comprising normalizing the range of the plurality ofapplications.
 33. The system according to claim 30, further comprising atag module for generating a tag for the insurance application, whereinthe tag is generated based on the previous insurance applications andtheir associated underwriting decisions.